Category-based sampling of machine learning data

ABSTRACT

A determination is made at a machine learning service that a training data set comprising a majority category of observation records and one or more minority categories of observation records meets a criterion for automated sampling. A sampling ratio to be used for a particular category of the majority category and the one or more minority categories is identified. A selected sampling methodology is applied to the particular category to obtain a sample in accordance with the sampling ratio. A particular machine learning model is trained using a result of applying at least the selected sampling methodology on the particular category.

BACKGROUND

Machine learning combines techniques from statistics and artificialintelligence to create algorithms that can learn from empirical data andgeneralize to solve problems in various domains such as natural languageprocessing, financial fraud detection, terrorism threat level detection,human health diagnosis and the like. In recent years, more and more rawdata that can potentially be utilized for machine learning models isbeing collected from a large variety of sources, such as sensors ofvarious kinds, web server logs, social media services, financialtransaction records, security cameras, and the like.

Traditionally, expertise in statistics and in artificial intelligencehas been a prerequisite for developing and using machine learningmodels. For many business analysts and even for highly qualified subjectmatter experts, the difficulty of acquiring such expertise is sometimestoo high a barrier to be able to take full advantage of the largeamounts of data potentially available to make improved businesspredictions and decisions. Furthermore, many machine learning techniquescan be computationally intensive, and in at least some cases it can behard to predict exactly how much computing power may be required forvarious phases of the techniques. Given such unpredictability, it maynot always be advisable or viable for business organizations to buildout their own machine learning computational facilities.

The quality of the results obtained from machine learning algorithms maydepend on how well the empirical data used for training the modelscaptures key relationships among different variables represented in thedata, and on how effectively and efficiently these relationships can beidentified. Depending on the nature of the problem that is to be solvedusing machine learning, very large data sets may have to be analyzed inorder to be able to make accurate predictions, especially predictions ofrelatively infrequent but significant events. For example, in financialfraud detection applications, where the number of fraudulenttransactions is typically a very small fraction of the total number oftransactions, identifying factors that can be used to label atransaction as fraudulent may potentially require analysis of millionsof transaction records, each representing dozens or even hundreds ofvariables. Constraints on raw input data set size, cleansing ornormalizing large numbers of potentially incomplete or error-containingrecords, and/or on the ability to extract representative subsets of theraw data also represent barriers that are not easy to overcome for manypotential beneficiaries of machine learning techniques. For many machinelearning problems, transformations may have to be applied on variousinput data variables before the data can be used effectively to trainmodels. In some traditional machine learning environments, themechanisms available to apply such transformations may be less thanoptimal—e.g., similar transformations may sometimes have to be appliedone by one to many different variables of a data set, potentiallyrequiring a lot of tedious and error-prone work.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example system environment in which variouscomponents of a machine learning service may be implemented, accordingto at least some embodiments.

FIG. 2 illustrates an example of a machine learning service implementedusing a plurality of network-accessible services of a provider network,according to at least some embodiments.

FIG. 3 illustrates an example of the use of a plurality of availabilitycontainers and security containers of a provider network for a machinelearning service, according to at least some embodiments.

FIG. 4 illustrates examples of a plurality of processing plans andcorresponding resource sets that may be generated at a machine learningservice, according to at least some embodiments.

FIG. 5 illustrates an example of asynchronous scheduling of jobs at amachine learning service, according to at least some embodiments.

FIG. 6 illustrates example artifacts that may be generated and storedusing a machine learning service, according to at least someembodiments.

FIG. 7 illustrates an example of automated generation of statistics inresponse to a client request to instantiate a data source, according toat least some embodiments.

FIG. 8 illustrates several model usage modes that may be supported at amachine learning service, according to at least some embodiments.

FIGS. 9a and 9b are flow diagrams illustrating aspects of operationsthat may be performed at a machine learning service that supportsasynchronous scheduling of machine learning jobs, according to at leastsome embodiments.

FIG. 10a is a flow diagram illustrating aspects of operations that maybe performed at a machine learning service at which a set of idempotentprogrammatic interfaces are supported, according to at least someembodiments.

FIG. 10b is a flow diagram illustrating aspects of operations that maybe performed at a machine learning service to collect and disseminateinformation about best practices related to different problem domains,according to at least some embodiments.

FIG. 11 illustrates examples interactions associated with the use ofrecipes for data transformations at a machine learning service,according to at least some embodiments.

FIG. 12 illustrates example sections of a recipe, according to at leastsome embodiments.

FIG. 13 illustrates an example grammar that may be used to define recipesyntax, according to at least some embodiments.

FIG. 14 illustrates an example of an abstract syntax tree that may begenerated for a portion of a recipe, according to at least someembodiments.

FIG. 15 illustrates an example of a programmatic interface that may beused to search for domain-specific recipes available from a machinelearning service, according to at least some embodiments.

FIG. 16 illustrates an example of a machine learning service thatautomatically explores a range of parameter settings for recipetransformations on behalf of a client, and selects acceptable orrecommended parameter settings based on results of such explorations,according to at least some embodiments.

FIG. 17 is a flow diagram illustrating aspects of operations that may beperformed at a machine learning service that supports re-usable recipesfor data set transformations, according to at least some embodiments.

FIG. 18 illustrates an example procedure for performing efficientin-memory filtering operations on a large input data set by a machinelearning service, according to at least some embodiments.

FIG. 19 illustrates tradeoffs associated with varying the chunk sizeused for filtering operation sequences on machine learning data sets,according to at least some embodiments.

FIG. 20a illustrates an example sequence of chunk-level filteringoperations, including a shuffle followed by a split, according to atleast some embodiments.

FIG. 20b illustrates an example sequence of in-memory filteringoperations that includes chunk-level filtering as well as intra-chunkfiltering, according to at least some embodiments.

FIG. 21 illustrates examples of alternative approaches to in-memorysampling of a data set, according to at least some embodiments.

FIG. 22 illustrates examples of determining chunk boundaries based onthe location of observation record boundaries, according to at leastsome embodiments.

FIG. 23 illustrates examples of jobs that may be scheduled at a machinelearning service in response to a request for extraction of data recordsfrom any of a variety of data source types, according to at least someembodiments.

FIG. 24 illustrates examples constituent elements of a record retrievalrequest that may be submitted by a client using a programmatic interfaceof an I/O (input-output) library implemented by a machine learningservice, according to at least some embodiments.

FIG. 25 is a flow diagram illustrating aspects of operations that may beperformed at a machine learning service that implements an I/O libraryfor in-memory filtering operation sequences on large input data sets,according to at least some embodiments.

FIG. 26 illustrates an example of an iterative procedure that may beused to improve the quality of predictions made by a machine learningmodel, according to at least some embodiments.

FIG. 27 illustrates an example of data set splits that may be used forcross-validation of a machine learning model, according to at least someembodiments.

FIG. 28 illustrates examples of consistent chunk-level splits of inputdata sets for cross validation that may be performed using a sequence ofpseudo-random numbers, according to at least some embodiments.

FIG. 29 illustrates an example of an inconsistent chunk-level split ofan input data set that may occur as a result of inappropriatelyresetting a pseudo-random number generator, according to at least someembodiments.

FIG. 30 illustrates an example timeline of scheduling related pairs oftraining and evaluation jobs, according to at least some embodiments.

FIG. 31 illustrates an example of a system in which consistency metadatais generated at a machine learning service in response to a clientrequest, according to at least some embodiments.

FIG. 32 is a flow diagram illustrating aspects of operations that may beperformed at a machine learning service in response to a request fortraining and evaluation iterations of a machine learning model,according to at least some embodiments.

FIG. 33 illustrates an example of an imbalanced training set that may beused at a machine learning service, according to at least someembodiments.

FIG. 34 illustrates an example of category-based sampling of a trainingset, according to at least some embodiments.

FIG. 35 illustrates examples of factors that may influence a selectionof sampling parameters to be used for a training set, according to atleast some embodiments.

FIG. 36 illustrates an example sequence of interactions between a clientand a machine learning service configured to automate category-basedsampling of data sets, according to at least some embodiments.

FIGS. 37a and 37b illustrate respective sequences of operations in whichclustering may be used together with category-based sampling to trainmodels at a machine learning service, according to at least someembodiments.

FIG. 38 is a flow diagram illustrating aspects of operations that may beperformed at a machine learning service that provides automatedcategory-based sampling of imbalanced data sets, according to at leastsome embodiments.

FIG. 39 is a flow diagram illustrating aspects of operations that may beperformed at a machine learning service to perform sampling iterationsusing error-based sampling weights, according to at least someembodiments.

FIG. 40 is a block diagram illustrating an example computing device thatmay be used in at least some embodiments.

While embodiments are described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that embodiments are not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit embodiments tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope as defined by the appended claims. The headings usedherein are for organizational purposes only and are not meant to be usedto limit the scope of the description or the claims. As used throughoutthis application, the word “may” is used in a permissive sense (i.e.,meaning having the potential to), rather than the mandatory sense (i.e.,meaning must). Similarly, the words “include,” “including,” and“includes” mean including, but not limited to.

DETAILED DESCRIPTION

Various embodiments of methods and apparatus for a customizable,easy-to-use machine learning service (MLS) designed to support largenumbers of users and a wide variety of algorithms and problem sizes aredescribed. In one embodiment, a number of MLS programmatic interfaces(such as application programming interfaces (APIs)) may be defined bythe service, which guide non-expert users to start using machinelearning best practices relatively quickly, without the users having toexpend a lot of time and effort on tuning models, or on learningadvanced statistics or artificial intelligence techniques. Theinterfaces may, for example, allow non-experts to rely on defaultsettings or parameters for various aspects of the procedures used forbuilding, training and using machine learning models, where the defaultsare derived from the accumulated experience of other practitionersaddressing similar types of machine learning problems. At the same time,expert users may customize the parameters or settings they wish to usefor various types of machine learning tasks, such as input recordhandling, feature processing, model building, execution and evaluation.In at least some embodiments, in addition to or instead of usingpre-defined libraries implementing various types of machine learningtasks, MLS clients may be able to extend the built-in capabilities ofthe service, e.g., by registering their own customized functions withthe service. Depending on the business needs or goals of the clientsthat implement such customized modules or functions, the modules may insome cases be shared with other users of the service, while in othercases the use of the customized modules may be restricted to theirimplementers/owners.

In some embodiments, a relatively straightforward recipe language may besupported, allowing MLS users to indicate various feature processingsteps that they wish to have applied on data sets. Such recipes may bespecified in text format, and then compiled into executable formats thatcan be re-used with different data sets on different resource sets asneeded. In at least some embodiments, the MLS may be implemented at aprovider network that comprises numerous data centers with hundreds ofthousands of computing and storage devices distributed around the world,allowing machine learning problems with terabyte-scale or petabyte-scaledata sets and correspondingly large compute requirements to be addressedin a relatively transparent fashion while still ensuring high levels ofisolation and security for sensitive data. Pre-existing services of theprovider network, such as storage services that support arbitrarilylarge data objects accessible via web service interfaces, databaseservices, virtual computing services, parallel-computing services,high-performance computing services, load-balancing services, and thelike may be used for various machine learning tasks in at least someembodiments. For MLS clients that have high availability and datadurability requirements, machine learning data (e.g., raw input data,transformed/manipulated input data, intermediate results, or finalresults) and/or models may be replicated across different geographicallocations or availability containers as described below. To meet an MLSclient's data security needs, selected data sets, models or codeimplementing user-defined functions or third-party functions may berestricted to security containers defined by the provider network insome embodiments, in which for example the client's machine learningtasks are executed in an isolated, single-tenant fashion instead of themulti-tenant approach that may typically be used for some of theprovider network's services. The term “MLS control plane” may be usedherein to refer to a collection of hardware and/or software entitiesthat are responsible for implementing various types of machine learningfunctionality on behalf of clients of the MLS, and for administrativetasks not necessarily visible to external MLS clients, such as ensuringthat an adequate set of resources is provisioned to meet client demands,detecting and recovering from failures, generating bills, and so on. Theterm “MLS data plane” may refer to the pathways and resources used forthe processing, transfer, and storage of the input data used forclient-requested operations, as well as the processing, transfer andstorage of output data produced as a result of client-requestedoperations.

According to some embodiments, a number of different types of entitiesrelated to machine learning tasks may be generated, modified, read,executed, and/or queried/searched via MLS programmatic interfaces.Supported entity types in one embodiment may include, among others, datasources (e.g., descriptors of locations or objects from which inputrecords for machine learning can be obtained), sets of statisticsgenerated by analyzing the input data, recipes (e.g., descriptors offeature processing transformations to be applied to input data fortraining models), processing plans (e.g., templates for executingvarious machine learning tasks), models (which may also be referred toas predictors), parameter sets to be used for recipes and/or models,model execution results such as predictions or evaluations, onlineaccess points for models that are to be used on streaming or real-timedata, and/or aliases (e.g., pointers to model versions that have been“published” for use as described below). Instances of these entity typesmay be referred to as machine learning artifacts herein—for example, aspecific recipe or a specific model may each be considered an artifact.Each of the entity types is discussed in further detail below.

The MLS programmatic interfaces may enable users to submit respectiverequests for several related tasks of a given machine learning workflow,such as tasks for extracting records from data sources, generatingstatistics on the records, feature processing, model training,prediction, and so on. A given invocation of a programmatic interface(such as an API) may correspond to a request for one or more operationsor tasks on one or more instances of a supported type of entity. Sometasks (and the corresponding APIs) may involve multiple different entitytypes—e.g., an API requesting a creation of a data source may result inthe generation of a data source entity instance as well as a statisticsentity instance. Some of the tasks of a given workflow may be dependenton the results of other tasks. Depending on the amount of data, and/oron the nature of the processing to be performed, some tasks may takehours or even days to complete. In at least some embodiments, anasynchronous approach may be taken to scheduling the tasks, in which MLSclients can submit additional tasks that depend on the output ofearlier-submitted tasks without waiting for the earlier-submitted tasksto complete. For example, a client may submit respective requests fortasks T2 and T3 before an earlier-submitted task T1 completes, eventhough the execution of T2 depends at least partly on the results of T1,and the execution of T3 depends at least partly on the results of T2. Insuch embodiments, the MLS may take care of ensuring that a given task isscheduled for execution only when its dependencies (if any dependenciesexist) have been met.

A queue or collection of job objects may be used for storing internalrepresentations of requested tasks in some implementations. The term“task”, as used herein, refers to a set of logical operationscorresponding to a given request from a client, while the term “job”refers to the internal representation of a task within the MLS. In someembodiments, a given job object may represent the operations to beperformed as a result of a client's invocation of a particularprogrammatic interface, as well as dependencies on other jobs. The MLSmay be responsible for ensuring that the dependencies of a given jobhave been met before the corresponding operations are initiated. The MLSmay also be responsible in such embodiments for generating a processingplan for each job, identifying the appropriate set of resources (e.g.,CPUs/cores, storage or memory) for the plan, scheduling the execution ofthe plan, gathering results, providing/saving the results in anappropriate destination, and at least in some cases for providing statusupdates or responses to the requesting clients. The MLS may also beresponsible in some embodiments for ensuring that the execution of oneclient's jobs do not affect or interfere with the execution of otherclients' jobs. In some embodiments, partial dependencies among tasks maybe supported—e.g., in a sequence of tasks (T1, T2, T3), T2 may depend onpartial completion of T1, and T2 may therefore be scheduled before T1completes. For example, T1 may comprise two phases or passes P1 and P2of statistics calculations, and T2 may be able to proceed as soon asphase P1 is completed, without waiting for phase P2 to complete. Partialresults of T1 (e.g., at least some statistics computed during phase P1)may be provided to the requesting client as soon as they becomeavailable in some cases, instead of waiting for the entire task to becompleted. A single shared queue that includes jobs corresponding torequests from a plurality of clients of the MLS may be used in someimplementations, while in other implementations respective queues may beused for different clients. In some implementations, lists or other datastructures that can be used to model object collections may be used ascontainers of to-be-scheduled jobs instead of or in addition to queues.In some embodiments, a single API request from a client may lead to thegeneration of several different job objects by the MLS. In at least oneembodiment, not all client API requests may be implemented usingjobs—e.g., a relatively short or lightweight task may be performedsynchronously with respect to the corresponding request, withoutincurring the overhead of job creation and asynchronous job scheduling.

The APIs implemented by the MLS may in some embodiments allow clients tosubmit requests to create, query the attributes of, read, update/modify,search, or delete an instance of at least some of the various entitytypes supported. For example, for the entity type “DataSource”,respective APIs similar to “createDataSource”, “describeDataSource” (toobtain the values of attributes of the data source), “updateDataSource”,“searchForDataSource”, and “deleteDataSource” may be supported by theMLS. A similar set of APIs may be supported for recipes, models, and soon. Some entity types may also have APIs for executing or running theentities, such as “executeModel” or “executeRecipe” in variousembodiments. The APIs may be designed to be largely easy to learn andself-documenting (e.g., such that the correct way to use a given API isobvious to non-experts), with an emphasis on making it simple to performthe most common tasks without making it too hard to perform more complextasks. In at least some embodiments multiple versions of the APIs may besupported: e.g., one version for a wire protocol (at the applicationlevel of a networking stack), another version as a Java™ library or SDK(software development kit), another version as a Python library, and soon. API requests may be submitted by clients using HTTP (HypertextTransfer Protocol), HTTPS (secure HTTP), Javascript, XML, or the like invarious implementations.

In some embodiments, some machine learning models may be created andtrained, e.g., by a group of model developers or data scientists usingthe MLS APIs, and then published for use by another community of users.In order to facilitate publishing of models for use by a wider audiencethan just the creators of the model, while preventing potentiallyunsuitable modifications to the models by unskilled members of the wideraudience, the “alias” entity type may be supported in such embodiments.In one embodiment, an alias may comprise an immutable name (e.g.,“SentimentAnalysisModel1”) and a pointer to a model that has alreadybeen created and stored in an MLS artifact repository (e.g.,“samModel-23adf-2013-12-13-08-06-01”, an internal identifier generatedfor the model by the MLS). Different sets of permissions on aliases maybe granted to model developers than are granted to the users to whom thealiases are being made available for execution. For example, in oneimplementation, members of a business analyst group may be allowed torun the model using its alias name, but may not be allowed to change thepointer, while model developers may be allowed to modify the pointerand/or modify the underlying model. For the business analysts, themachine learning model exposed via the alias may represent a “black box”tool, already validated by experts, which is expected to provide usefulpredictions for various input data sets. The business analysts may notbe particularly concerned about the internal working of such a model.The model developers may continue to experiment with various algorithms,parameters and/or input data sets to obtain improved versions of theunderlying model, and may be able to change the pointer to point to anenhanced version to improve the quality of predictions obtained by thebusiness analysts. In at least some embodiments, to isolate alias usersfrom changes to the underlying models, the MLS may guarantee that (a) analias can only point to a model that has been successfully trained and(b) when an alias pointer is changed, both the original model and thenew model (i.e., the respective models being pointed to by the oldpointer and the new pointer) consume the same type of input and providethe same type of prediction (e.g., binary classification, multi-classclassification or regression). In some implementations, a given modelmay itself be designated as un-modifiable if an alias is created forit—e.g., the model referred to by the pointer“samModel-23adf-2013-12-13-08-06-01” may no longer be modified even byits developers after the alias is created in such an implementation.Such clean separation of roles and capabilities with respect to modeldevelopment and use may allow larger audiences within a businessorganization to benefit from machine learning models than simply thoseskilled enough to develop the models.

A number of choices may be available with respect to the manner in whichthe operations corresponding to a given job are mapped to MLS servers.For example, it may be possible to partition the work required for agiven job among many different servers to achieve better performance. Aspart of developing the processing plan for a job, the MLS may select aworkload distribution strategy for the job in some embodiments. Theparameters determined for workload distribution in various embodimentsmay differ based on the nature of the job. Such factors may include, forexample, (a) determining a number of passes of processing, (b)determining a parallelization level (e.g., the number of “mappers” and“reducers” in the case of a job that is to be implemented using theMap-Reduce technique), (c) determining a convergence criterion to beused to terminate the job, (d) determining a target durability level forintermediate data produced during the job, or (e) determining a resourcecapacity limit for the job (e.g., a maximum number of servers that canbe assigned to the job based on the number of servers available in MLSserver pools, or on the client's budget limit). After the workloadstrategy is selected, the actual set of resources to be used may beidentified in accordance with the strategy, and the job's operations maybe scheduled on the identified resources. In some embodiments, a pool ofcompute servers and/or storage servers may be pre-configured for theMLS, and the resources for a given job may be selected from such a pool.In other embodiments, the resources may be selected from a pool assignedto the client on whose behalf the job is to be executed—e.g., the clientmay acquire resources from a computing service of the provider networkprior to submitting API requests, and may provide an indication of theacquired resources to the MLS for job scheduling. If client-providedcode (e.g., code that has not necessarily been thoroughly tested by theMLS, and/or is not included in the MLS's libraries) is being used for agiven job, in some embodiments the client may be required to acquire theresources to be used for the job, so that any side effects of runningthe client-provided code may be restricted to the client's own resourcesinstead of potentially affecting other clients.

Example System Environments

FIG. 1 illustrates an example system environment in which variouscomponents of a machine learning service (MLS) may be implemented,according to at least some embodiments. In system 100, the MLS mayimplement a set of programmatic interfaces 161 (e.g., APIs, command-linetools, web pages, or standalone GUIs) that can be used by clients 164(e.g., hardware or software entities owned by or assigned to customersof the MLS) to submit requests 111 for a variety of machine learningtasks or operations. The administrative or control plane portion of theMLS may include MLS request handler 180, which accepts the clientrequests 111 and inserts corresponding job objects into MLS job queue142, as indicated by arrow 112. In general, the control plane of the MLSmay comprise a plurality of components (including the request handler,workload distribution strategy selectors, one or more job schedulers,metrics collectors, and modules that act as interfaces with otherservices) which may also be referred to collectively as the MLS manager.The data plane of the MLS may include, for example, at least a subset ofthe servers of pool(s) 185, storage devices that are used to store inputdata sets, intermediate results or final results (some of which may bepart of the MLS artifact repository), and the network pathways used fortransferring client input data and results.

As mentioned earlier, each job object may indicate one or moreoperations that are to be performed as a result of the invocation of aprogrammatic interface 161, and the scheduling of a given job may insome cases depend upon the successful completion of at least a subset ofthe operations of an earlier-generated job. In at least someimplementations, job queue 142 may be managed as a first-in-first-out(FIFO) queue, with the further constraint that the dependencyrequirements of a given job must have been met in order for that job tobe removed from the queue. In some embodiments, jobs created on behalfof several different clients may be placed in a single queue, while inother embodiments multiple queues may be maintained (e.g., one queue ineach data center of the provider network being used, or one queue perMLS customer). Asynchronously with respect to the submission of therequests 111, the next job whose dependency requirements have been metmay be removed from job queue 142 in the depicted embodiment, asindicated by arrow 113, and a processing plan comprising a workloaddistribution strategy may be identified for it. The workloaddistribution strategy layer 175, which may also be a component of theMLS control plane as mentioned earlier, may determine the manner inwhich the lower level operations of the job are to be distributed amongone or more compute servers (e.g., servers selected from pool 185),and/or the manner in which the data analyzed or manipulated for the jobis to be distributed among one or more storage devices or servers. Afterthe processing plan has been generated and the appropriate set ofresources to be utilized for the job has been identified, the job'soperations may be scheduled on the resources. Results of some jobs maybe stored as MLS artifacts within repository 120 in some embodiments, asindicated by arrow 142.

In at least one embodiment, some relatively simple types of clientrequests 111 may result in the immediate generation, retrieval, storage,or modification of corresponding artifacts within MLS artifactrepository 120 by the MLS request handler 180 (as indicated by arrow141). Thus, the insertion of a job object in job queue 142 may not berequired for all types of client requests. For example, a creation orremoval of an alias for an existing model may not require the creationof a new job in such embodiments. In the embodiment shown in FIG. 1,clients 164 may be able to view at least a subset of the artifactsstored in repository 120, e.g., by issuing read requests 118 viaprogrammatic interfaces 161.

A client request 111 may indicate one or more parameters that may beused by the MLS to perform the operations, such as a data sourcedefinition 150, a feature processing transformation recipe 152, orparameters 154 to be used for a particular machine learning algorithm.In some embodiments, artifacts respectively representing the parametersmay also be stored in repository 120. Some machine learning workflows,which may correspond to a sequence of API requests from a client 164,may include the extraction and cleansing of input data records from rawdata repositories 130 (e.g., repositories indicated in data sourcedefinitions 150) by input record handlers 160 of the MLS, as indicatedby arrow 114. This first portion of the workflow may be initiated inresponse to a particular API invocation from a client 164, and may beexecuted using a first set of resources from pool 185. The input recordhandlers may, for example, perform such tasks as splitting the datarecords, sampling the data records, and so on, in accordance with a setof functions defined in an I/O (input/output) library of the MLS. Theinput data may comprise data records that include variables of any of avariety of data types, such as, for example text, a numeric data type(e.g., real or integer), Boolean, a binary data type, a categorical datatype, an image processing data type, an audio processing data type, abioinformatics data type, a structured data type such as a data typecompliant with the Unstructured Information Management Architecture(UIMA), and so on. In at least some embodiments, the input data reachingthe MLS may be encrypted or compressed, and the MLS input data handlingmachinery may have to perform decryption or decompression before theinput data records can be used for machine learning tasks. In someembodiments in which encryption is used, MLS clients may have to providedecryption metadata (e.g., keys, passwords, or other credentials) to theMLS to allow the MLS to decrypt data records. Similarly, an indicationof the compression technique used may be provided by the clients in someimplementations to enable the MLS to decompress the input data recordsappropriately. The output produced by the input record handlers may befed to feature processors 162 (as indicated by arrow 115), where a setof transformation operations may be performed 162 in accordance withrecipes 152 using another set of resources from pool 185. Any of avariety of feature processing approaches may be used depending on theproblem domain: e.g., the recipes typically used for computer visionproblems may differ from those used for voice recognition problems,natural language processing, and so on. The output 116 of the featureprocessing transformations may in turn be used as input for a selectedmachine learning algorithm 166, which may be executed in accordance withalgorithm parameters 154 using yet another set of resources from pool185. A wide variety of machine learning algorithms may be supportednatively by the MLS libraries, including for example random forestalgorithms, neural network algorithms, stochastic gradient descentalgorithms, and the like. In at least one embodiment, the MLS may bedesigned to be extensible—e.g., clients may provide or register theirown modules (which may be defined as user-defined functions) for inputrecord handling, feature processing, or for implementing additionalmachine learning algorithms than are supported natively by the MLS. Insome embodiments, some of the intermediate results (e.g., summarizedstatistics produced by the input record handlers) of a machine learningworkflow may be stored in MLS artifact repository 120.

In the embodiment depicted in FIG. 1, the MLS may maintain knowledgebase 122 containing information on best practices for various machinelearning tasks. Entries may be added into the best practices KB 122 byvarious control-plane components of the MLS, e.g., based on metricscollected from server pools 185, feedback provided by clients 164, andso on. Clients 164 may be able to search for and retrieve KB entries viaprogrammatic interfaces 161, as indicated by arrow 117, and may use theinformation contained in the entries to select parameters (such asspecific recipes or algorithms to be used) for their requestsubmissions. In at least some embodiments, new APIs may be implemented(or default values for API parameters may be selected) by the MLS on thebasis of best practices identified over time for various types ofmachine learning practices.

FIG. 2 illustrates an example of a machine learning service implementedusing a plurality of network-accessible services of a provider network,according to at least some embodiments. Networks set up by an entitysuch as a company or a public sector organization to provide one or moreservices (such as various types of multi-tenant and/or single-tenantcloud-based computing or storage services) accessible via the Internetand/or other networks to a distributed set of clients may be termedprovider networks herein. A given provider network may include numerousdata centers hosting various resource pools, such as collections ofphysical and/or virtualized computer servers, storage devices,networking equipment and the like, needed to implement, configure anddistribute the infrastructure and services offered by the provider. Atleast some provider networks and the corresponding network-accessibleservices may be referred to as “public clouds” and “public cloudservices” respectively. Within large provider networks, some datacenters may be located in different cities, states or countries thanothers, and in some embodiments the resources allocated to a givenservice such as the MLS may be distributed among several such locationsto achieve desired levels of availability, fault-resilience andperformance, as described below in greater detail with reference to FIG.3.

In the embodiment shown in FIG. 2, the MLS utilizes storage service 202,computing service 258, and database service 255 of provider network 202.At least some of these services may also be used concurrently by othercustomers (e.g., other services implemented at the provider network,and/or external customers outside the provider network) in the depictedembodiment, i.e., the services may not be restricted to MLS use. MLSgateway 222 may be established to receive client requests 210 submittedover external network 206 (such as portions of the Internet) by clients164. MLS gateway 222 may, for example, be configured with a set ofpublicly accessible IP (Internet Protocol) addresses that can be used toaccess the MLS. The client requests may be formatted in accordance witha representational state transfer (REST) API implemented by the MLS insome embodiments. In one embodiment, MLS customers may be provided anSDK (software development kit) 204 for local installation at clientcomputing devices, and the requests 210 may be submitted from withinprograms written in conformance with the SDK. A client may also orinstead access MLS functions from a compute server 262 of computingservice 262 that has been allocated to the client in variousembodiments.

Storage service 252 may, for example, implement a web services interfacethat can be used to create and manipulate unstructured data objects ofarbitrary size. Database service 255 may implement either relational ornon-relational databases. The storage service 252 and/or the databaseservice 255 may play a variety of roles with respect to the MLS in thedepicted embodiment. The MLS may require clients 164 to define datasources within the provider network boundary for their machine learningtasks in some embodiments. In such a scenario, clients may firsttransfer data from external data sources 229 into internal data sourceswithin the provider network, such as internal data source 230A managedby storage service 252, or internal data source 230B managed by databaseservice 255. In some cases, the clients of the MLS may already be usingthe provider network services for other applications, and some of theoutput of those applications (e.g., web server logs or video files),saved at the storage service 252 or the database service 255, may serveas the data sources for MLS workflows.

In response to at least some client requests 210, the MLS requesthandler 180 may generate and store corresponding job objects within ajob queue 142, as discussed above. In the embodiment depicted in FIG. 2,the job queue 142 may itself be represented by a database object (e.g.,a table) stored at database service 255. A job scheduler 272 mayretrieve a job from queue 142, e.g., after checking that the job'sdependency requirements have been met, and identify one or more servers262 from computing service 258 to execute the job's computationaloperations. Input data for the computations may be read from theinternal or external data sources by the servers 262. The MLS artifactrepository 220 may be implemented within the database service 255(and/or within the storage service 252) in various embodiments. In someembodiments, intermediate or final results of various machine learningtasks may also be stored within the storage service 252 and/or thedatabase service 255.

Other services of the provider network, e.g., including load balancingservices, parallel computing services, automated scaling services,and/or identity management services, may also be used by the MLS in someembodiments. A load balancing service may, for example, be used toautomatically distribute computational load among a set of servers 262.A parallel computing service that implements the Map-reduce programmingmodel may be used for some types of machine learning tasks. Automatedscaling services may be used to add or remove servers assigned to aparticular long-lasting machine learning task. Authorization andauthentication of client requests may be performed with the help of anidentity management service of the provider network in some embodiments.

In some embodiments a provider network may be organized into a pluralityof geographical regions, and each region may include one or moreavailability containers, which may also be termed “availability zones”.An availability container in turn may comprise portions or all of one ormore distinct physical premises or data centers, engineered in such away (e.g., with independent infrastructure components such aspower-related equipment, cooling equipment, and/or physical securitycomponents) that the resources in a given availability container areinsulated from failures in other availability containers. A failure inone availability container may not be expected to result in a failure inany other availability container; thus, the availability profile of agiven physical host or server is intended to be independent of theavailability profile of other hosts or servers in a differentavailability container.

In addition to their distribution among different availabilitycontainers, provider network resources may also be partitioned intodistinct security containers in some embodiments. For example, while ingeneral various types of servers of the provider network may be sharedamong different customers' applications, some resources may berestricted for use by a single customer. A security policy may bedefined to ensure that specified group of resources (which may includeresources managed by several different provider network services, suchas a computing service, a storage service, or a database service, forexample) are only used by a specified customer or a specified set ofclients. Such a group of resources may be referred to as “securitycontainers” or “security groups” herein.

FIG. 3 illustrates an example of the use of a plurality of availabilitycontainers and security containers of a provider network for a machinelearning service, according to at least some embodiments. In thedepicted embodiment, provider network 302 comprises availabilitycontainers 366A, 366B and 366C, each of which may comprise portions orall of one or more data centers. Each availability container 366 has itsown set of MLS control-plane components 344: e.g., control planecomponents 344A-344C in availability containers 366A-366C respectively.The control plane components in a given availability container mayinclude, for example, an instance of an MLS request handler, one or moreMLS job queues, a job scheduler, workload distribution components, andso on. The control plane components in different availability containersmay communicate with each other as needed, e.g., to coordinate tasksthat utilize resources at more than one data center. Each availabilitycontainer 366 has a respective pool 322 (e.g., 322A-322C) of MLS serversto be used in a multi-tenant fashion. The servers of the pools 322 mayeach be used to perform a variety of MLS operations, potentially fordifferent MLS clients concurrently. In contrast, for executing MLS tasksthat require a higher level of security or isolation, single-tenantserver pools that are designated for only a single client's workload maybe used, such as single tenant server pools 330A, 330B and 330C. Pools330A and 330B belong to security container 390A, while pool 330C is partof security container 390B. Security container 390A may be usedexclusively for a customer C1 (e.g., to run customer-provided machinelearning modules, or third-party modules specified by the customer),while security container 390B may be used exclusively for a differentcustomer C2 in the depicted example.

In some embodiments, at least some of the resources used by the MLS maybe arranged in redundancy groups that cross availability containerboundaries, such that MLS tasks can continue despite a failure thataffects MLS resources of a given availability container. For example, inone embodiment, a redundancy group RG1 comprising at least one server S1in availability container 366A, and at least one server S2 inavailability container 366B may be established, such that S1'sMLS-related workload may be failed over to S2 (or vice versa). Forlong-lasting MLS tasks (such as tasks that involve terabyte orpetabyte-scale data sets), the state of a given MLS job may becheck-pointed to persistent storage (e.g., at a storage service or adatabase service of the provider network that is also designed towithstand single-availability-container failures) periodically, so thata failover server can resume a partially-completed task from the mostrecent checkpoint instead of having to start over from the beginning.The storage service and/or the database service of the provider networkmay inherently provide very high levels of data durability, e.g., usingerasure coding or other replication techniques, so the data sets may notnecessarily have to be copied in the event of a failure. In someembodiments, clients of the MLS may be able to specify the levels ofdata durability desired for their input data sets, intermediate datasets, artifacts, and the like, as well as the level of compute serveravailability desired. The MLS control plane may determine, based on theclient requirements, whether resources in multiple availabilitycontainers should be used for a given task or a given client. Thebilling amounts that the clients have to pay for various MLS tasks maybe based at least in part on their durability and availabilityrequirements. In some embodiments, some clients may indicate to the MLScontrol-plane that they only wish to use resources within a givenavailability container or a given security container. For certain typesof tasks, the costs of transmitting data sets and/or results over longdistances may be so high, or the time required for the transmissions mayso long, that the MLS may restrict the tasks to within a singlegeographical region of the provider network (or even within a singledata center).

Processing Plans

As mentioned earlier, the MLS control plane may be responsible forgenerating processing plans corresponding to each of the job objectsgenerated in response to client requests in at least some embodiments.For each processing plan, a corresponding set of resources may then haveto be identified to execute the plan, e.g., based on the workloaddistribution strategy selected for the plan, the available resources,and so on. FIG. 4 illustrates examples of various types of processingplans and corresponding resource sets that may be generated at a machinelearning service, according to at least some embodiments.

In the illustrated scenario, MLS job queue 142 comprises five jobs, eachcorresponding to the invocation of a respective API by a client. Job J1(shown at the head of the queue) was created in response to aninvocation of API1. Jobs J2 through J5 were created respectively inresponse to invocations of API2 through API5. Corresponding to job J1,an input data cleansing plan 422 may be generated, and the plan may beexecuted using resource set RS1. The input data cleansing plan mayinclude operations to read and validate the contents of a specified datasource, fill in missing values, identify and discard (or otherwiserespond to) input records containing errors, and so on. In some casesthe input data may also have to be decompressed, decrypted, or otherwisemanipulated before it can be read for cleansing purposes. Correspondingto job J2, a statistics generation plan 424 may be generated, andsubsequently executed on resource set RS2. The types of statistics to begenerated for each data attribute (e.g., mean, minimum, maximum,standard deviation, quantile binning, and so on for numeric attributes)and the manner in which the statistics are to be generated (e.g.,whether all the records generated by the data cleansing plan 422 are tobe used for the statistics, or a sub-sample is to be used) may beindicated in the statistics generation plan. The execution of job J2 maybe dependent on the completion of job J1 in the depicted embodiment,although the client request that led to the generation of job J2 mayhave been submitted well before J1 is completed.

A recipe-based feature processing plan 426 corresponding to job J3 (andAPI3) may be generated, and executed on resource set RS3. Furtherdetails regarding the syntax and management of recipes are providedbelow. Job J4 may result in the generation of a model training plan 428(which may in turn involve several iterations of training, e.g., withdifferent sets of parameters). The model training may be performed usingresource set RS4. Model execution plan 430 may correspond to job J5(resulting from the client's invocation of API5), and the model mayeventually be executed using resource set RS5. In some embodiments, thesame set of resources (or an overlapping set of resources) may be usedfor performing several or all of a client's jobs—e.g., the resource setsRS1-RS5 may not necessarily differ from one another. In at least oneembodiment, a client may indicate, e.g., via parameters included in anAPI call, various elements or properties of a desired processing plan,and the MLS may take such client preferences into account. For example,for a particular statistics generation job, a client may indicate that arandomly-selected sample of 25% of the cleansed input records may beused, and the MLS may generate a statistics generation plan thatincludes a step of generating a random sample of 25% of the dataaccordingly. In other cases, the MLS control plane may be given morefreedom to decide exactly how a particular job is to be implemented, andit may consult its knowledge base of best practices to select theparameters to be used.

Job Scheduling

FIG. 5 illustrates an example of asynchronous scheduling of jobs at amachine learning service, according to at least some embodiments. In thedepicted example, a client has invoked four MLS APIs, API1 through API4,and four corresponding job objects J1 through J4 are created and placedin job queue 142. Timelines TL1, TL2, and TL3 show the sequence ofevents from the perspective of the client that invokes the APIs, therequest handler that creates and inserts the jobs in queue 142, and ajob scheduler that removes the jobs from the queue and schedules thejobs at selected resources.

In the depicted embodiment, in addition to the base case of nodependency on other jobs, two types of inter job dependencies may besupported. In one case, termed “completion dependency”, the execution ofone job Jp cannot be started until another job Jq is completedsuccessfully (e.g., because the final output of Jq is required as inputfor Jp). Full dependency is indicated in FIG. 5 by the parameter“dependsOnComplete” shown in the job objects—e.g., J2 is dependent on J1completing execution, and J4 depends on J2 completing successfully. Inthe other type of dependency, the execution of one job Jp may be startedas soon as some specified phase of another job Jq is completed. Thislatter type of dependency may be termed a “partial dependency”, and isindicated in FIG. 5 by the “dependsOnPartial” parameter. For example, J3depends on the partial completion of J2, and J4 depends on the partialcompletion of J3. It is noted that in some embodiments, to simplify thescheduling, such phase-based dependencies may be handled by splitting ajob with N phases into N smaller jobs, thereby converting partialdependencies into full dependencies. J1 has no dependencies of eithertype in the depicted example.

As indicated on client timeline TL1, API1 through API4 may be invokedwithin the time period t0 to t1. Even though some of the operationsrequested by the client depend on the completion of operationscorresponding to earlier-invoked APIs, the MLS may allow the client tosubmit the dependent operation requests much earlier than the processingof the earlier-invoked APIs' jobs in the depicted embodiment. In atleast some embodiments, parameters specified by the client in the APIcalls may indicate the inter job dependencies. For example, in oneimplementation, in response to API1, the client may be provided with ajob identifier for J1, and that job identifier may be included as aparameter in API2 to indicate that the results of API1 are required toperform the operations corresponding to API2. As indicated by therequest handler's timeline TL2, the jobs corresponding to each API callmay be created and queued shortly after the API is invoked. Thus, allfour jobs have been generated and placed within the job queue 142 by ashort time after t1.

As shown in the job scheduler timeline TL3, job J1 may be scheduled forexecution at time t2. The delay between the insertion of J1 in queue 142(shortly after t0) and the scheduling of J1 may occur for a number ofreasons in the depicted embodiment—e.g., because there may have beenother jobs ahead of J1 in the queue 142, or because it takes some timeto generate a processing plan for J1 and identify the resources to beused for J1, or because enough resources were not available until t2.J1's execution lasts until t3. In the depicted embodiment, when J1completes, (a) the client is notified and (b) J2 is scheduled forexecution. As indicated by J2's dependsOnComplete parameter value, J2depends on J1's completion, and J2's execution could therefore not havebeen begun until t3, even if J2's processing plan were ready and J2'sresource set had been available prior to t3.

As indicated by J3's “dependsOnPartial” parameter value, J3 can bestarted when a specified phase or subset of J2's work is complete in thedepicted example. The portion of J2 upon which J3 depends completes attime t4 in the illustrated example, and the execution of J3 thereforebegins (in parallel with the execution of the remaining portion of J2)at t4. In the depicted example, the client may be notified at time t4regarding the partial completion of J2 (e.g., the results of thecompleted phase of J2 may be provided to the client).

At t5, the portion of J3 on which J4 depends may be complete, and theclient may be notified accordingly. However, J4 also depends on thecompletion of J2, so J4 cannot be started until J2 completes at t6. J3continues execution until t8. J4 completes at t7, earlier than t8. Theclient is notified regarding the completion of each of the jobscorresponding to the respective API invocations API1-API4 in thedepicted example scenario. In some embodiments, partial dependenciesbetween jobs may not be supported—instead, as mentioned earlier, in somecases such dependencies may be converted into full dependencies bysplitting multi-phase jobs into smaller jobs. In at least oneimplementation, instead of or in addition to being notified when thejobs corresponding to the API invocations are complete (or when phasesof the jobs are complete), clients may be able to submit queries to theMLS to determine the status (or the extent of completion) of theoperations corresponding to various API calls. For example, an MLS jobmonitoring web page may be implemented, enabling clients to view theprogress of their requests (e.g., via a “percent complete” indicator foreach job), expected completion times, and so on. In some embodiments, apolling mechanism may be used by clients to determine the progress orcompletion of the jobs.

MLS Artifacts

FIG. 6 illustrates example artifacts that may be generated and storedusing a machine learning service, according to at least someembodiments. In general, MLS artifacts may comprise any of the objectsthat may be stored in a persistent manner as a result of an invocationof an MLS programmatic interface. In some implementations, some APIparameters (e.g., text versions of recipes) that are passed to the MLSmay be stored as artifacts. As shown, in the depicted embodiment, MLSartifacts 601 may include, among others, data sources 602, statistics603, feature processing recipes 606, model predictions 608, evaluations610, modifiable or in-development models 630, and published models oraliases 640. In some implementations the MLS may generate a respectiveunique identifier for each instance of at least some of the types ofartifacts shown and provide the identifiers to the clients. Theidentifiers may subsequently be used by clients to refer to the artifact(e.g., in subsequent API calls, in status queries, and so on).

A client request to create a data source artifact 602 may include, forexample, an indication of an address or location from which data recordscan be read, and some indication of the format or schema of the datarecords. For example, an indication of a source URI (universal resourceidentifier) to which HTTP GET requests can be directed to retrieve thedata records, an address of a storage object at a provider networkstorage service, or a database table identifier may be provided. Theformat (e.g., the sequence and types of the fields or columns of thedata records) may be indicated in some implementations via a separatecomma separated variable (csv) file. In some embodiments, the MLS may beable to deduce at least part of the address and/or format informationneeded to create the data source artifact—e.g., based on the client'sidentifier, it may be possible to infer the root directory or root URIof the client's data source, and based on an analysis of the first fewrecords, it may be possible to deduce at least the data types of thecolumns of the schema. In some embodiments, the client request to createa data source may also include a request to re-arrange the raw inputdata, e.g., by sampling or splitting the data records using an I/Olibrary of the MLS. When requesting a creation of a data source, in someimplementations clients may also be required to provide securitycredentials that can be used by the MLS to access the data records.

In some embodiments, as described in further detail below with respectto FIG. 7, at least some statistics 603 may be generated automaticallyfor the data records of a data source. In other embodiments, the MLS mayalso or instead enable clients to explicitly request the generation ofvarious types of statistics, e.g., via the equivalent of acreateStatistics(dataSourceID, statisticsDescriptor) request in whichthe client indicates the types of statistics to be generated for aspecified data source. The types of statistics artifacts that aregenerated may vary based on the data types of the input recordvariables—e.g., for numeric variables, the mean, median, minimum,maximum, standard deviation, quantile bins, number of nulls or“not-applicable” values and the like may be generated. Cross-variablestatistics such as correlations may also be generated, eitherautomatically or on demand, in at least some embodiments.

Recipes 606 comprising feature processing transformation instructionsmay be provided by a client (or selected from among a set of availablerecipes accessible from an MLS recipe collection) in some embodiments. Arecipe language allowing clients to define groups of variables,assignments, dependencies upon other artifacts such as models, andtransformation outputs may be supported by the MLS in such embodiments,as described below in greater detail. Recipes submitted in text form maybe compiled into executable versions and re-used on a variety of datasets in some implementations.

At least two types of artifacts representing machine learning models orpredictors may be generated and stored in the depicted embodiment.Often, the process of developing and refining a model may take a longtime, as the developer may try to improve the accuracy of thepredictions using a variety of data sets and a variety of parameters.Some models may be improved over a number of weeks or months, forexample. In such scenarios it may be worthwhile to enable other users(e.g., business analysts) to utilize one version of a model, while modeldevelopers continue to generate other, improved versions. Accordingly,the artifacts representing models may belong to one of two categories insome embodiments: modifiable models 630, and published models or aliases640. An alias may comprise an alias name or identifier, and a pointer toa model (e.g., alias 640A points to model 630B, and alias 640B points tomodel 630D in the depicted embodiment). As used herein, the phrase“publishing a model” refers to making a particular version of a modelexecutable by a set of users by reference to an alias name oridentifier. In some cases, at least some of the users of the set may notbe permitted to modify the model or the alias. Non-expert users 678 maybe granted read and execute permissions to the aliases, while modeldevelopers 676 may also be allowed to modify models 630 (and/or thepointers of the aliases 640) in some embodiments. In some embodiments, aset of guarantees may be provided to alias users: e.g., that the formatof the input and output of an alias (and the underlying model referredto by the alias) will not change once the alias is published, and thatthe model developers have thoroughly tested and validated the underlyingmodel pointed to by the alias. In addition, a number of other logicalconstraints may be enforced with respect to aliases in such embodiments.For example, if the alias is created for a model used in online mode(model usage modes are described in further detail below with respect toFIG. 8), the MLS may guarantee that the model pointed to remains online(i.e., the model cannot be un-mounted). In some implementations adistinction may be drawn between aliases that are currently inproduction mode and those that are in internal-use or test mode, and theMLS may ensure that the underlying model is not deleted or un-mountedfor an alias in production mode. When creating aliases to online-modemodels, a minimum throughput rate of predictions/evaluations may bedetermined for the alias, and the MLS may ensure that the resourcesassigned to the model can meet the minimum throughput rate in someembodiments. After model developers 676 improve the accuracy and/orperformance characteristics of a newer version of a model 630 relativeto an older version for which an alias 640 has been created, they mayswitch the pointer of the alias so that it now points to the improvedversion. Thus, non-expert users may not have to change anything in theway that they have been using the aliases, while benefiting from theimprovements. In some embodiments, alias users may be able to submit aquery to learn when the underlying model was last changed, or may benotified when they request an execution of an alias that the underlyingmodel has been changes since the last execution.

Results of model executions, such as predictions 608 (values predictedby a model for a dependent variable in a scenario in which the actualvalues of the independent variable are not known) and model evaluations610 (measures of the accuracy of a model, computed when the predictionsof the model can be compared to known values of dependent variables) mayalso be stored as artifacts by the MLS in some embodiments. In additionto the artifact types illustrated in FIG. 6, other artifact types mayalso be supported in some embodiments—e.g., objects representing networkendpoints that can be used for real-time model execution on streamingdata (as opposed to batch-mode execution on a static set of data) may bestored as artifacts in some embodiments, and client session logs (e.g.,recordings of all the interactions between a client and the MLS during agiven session) may be stored as artifacts in other embodiments.

In some embodiments, the MLS may support recurring scheduling of relatedjobs. For example, a client may create an artifact such as a model, andmay want that same model to be re-trained and/or re-executed fordifferent input data sets (e.g., using the same configuration ofresources for each of the training or prediction iterations) atspecified points in time. In some cases the points in time may bespecified explicitly (e.g., by the client requesting the equivalent of“re-run model M1 on the currently available data set at data source DS1at 11:00, 15:00 and 19:00 every day”). In other cases the client mayindicate the conditions under which the iterations are to be scheduled(e.g., by the client requesting the equivalent of “re-run model M1whenever the next set of 1000000 new records becomes available from datasource DS1”). A respective job may be placed in the MLS job queue foreach recurring training or execution iteration. The MLS may implement aset of programmatic interface enabling such scheduled recurringoperations in some embodiments. Using such an interface, a client mayspecify a set of model/alias/recipe artifacts (or respective versions ofthe same underling artifact) to be used for each of the iterations,and/or the resource configurations to be used. Such programmaticinterfaces may be referred to as “pipelining APIs” in some embodiments.In addition to the artifact types shown in FIG. 6, pipeline artifactsmay be stored in the MLS artifact repository in some embodiments, witheach instance of a pipeline artifact representing a named set ofrecurring operations requested via such APIs. In one embodiment, aseparately-managed data pipelining service implemented at the providernetwork may be used in conjunction with the MLS for supporting suchrecurrent operations.

As mentioned above, in some embodiments, the MLS may automaticallygenerate statistics when a data source is created. FIG. 7 illustrates anexample of automated generation of statistics in response to a clientrequest to instantiate a data source, according to at least someembodiments. As shown, a client 764 submits a data source creationrequest 712 to the MLS control plane 780 via an MLS API 761. Thecreation request may specify an address or location from which datarecords can be retrieved, and optionally a schema or format documentindicating the columns or fields of the data records.

In response to receiving request 712, the MLS control plane 780 maygenerate and store a data source artifact 702 in the MLS artifactrepository. In addition, and depending in some cases on the currentavailability of resources at the MLS, the MLS may also initiate thegeneration of one or more statistics objects 730 in the depictedembodiment, even if the client request did not explicitly request suchstatistics. Any combination of a number of different types of statisticsmay be generated automatically in one of two modes in variousembodiments. For example, for very large data sets, an initial set ofstatistics 763 based on a sub-sample (e.g., a randomly-selected subsetof the large data set) may be obtained in a first phase, while thegeneration of full-sample statistics 764 derived from the entire dataset may be deferred to a second phase. Such a multi-phase approachtowards statistics generation may be implemented, for example, to allowthe client to get a rough or approximate summary of the data set valuesfairly rapidly in the first phase, so that the client may begin planningsubsequent machine learning workflow steps without waiting for astatistical analysis of the complete data set.

As shown, a variety of different statistics may be obtained in eitherphase. For numeric variables, basic statistics 765 may include the mean,median, minimum, maximum, and standard deviation. Numeric variables mayalso be binned (categorized into a set of ranges such as quartiles orquintiles); such bins 767 may be used for the construction of histogramsthat may be displayed to the client. Depending on the nature of thedistribution of the variable, either linear or logarithmic binboundaries may be selected. In some embodiments, correlations 768between different variables may be computed as well. In at least oneembodiment, the MLS may utilize the automatically generated statistics(such as the correlation values) to identify candidate groups 769 ofvariables that may have greater predictive power than others. Forexample, to avoid over-fitting for certain classes of models, only onevariable among a set of variables that correlate very strongly with oneanother may be recommended as a candidate for input to a model. In suchscenarios, the client may be able to avoid the time and effort requiredto explore the significance of other variables. In many problem domainsin which a given data record may have hundreds or even thousands ofvariables, such an automated selection of candidate variables expectedto have greater predictive effectiveness may be very valuable to clientsof the MLS.

FIG. 8 illustrates several model usage modes that may be supported at amachine learning service, according to at least some embodiments. Modelusage modes may be broadly classified into three categories: batch mode,online or real-time mode, and local mode. In batch mode, a given modelmay be run on a static set of data records. In real-time mode, a networkendpoint (e.g., an IP address) may be assigned as a destination to whichinput data records for a specified model are to be submitted, and modelpredictions may be generated on groups of streaming data records as therecords are received. In local mode, clients may receive executablerepresentations of a specified model that has been trained and validatedat the MLS, and the clients may run the models on computing devices oftheir choice (e.g., at devices located in client networks rather than inthe provider network where the MLS is implemented).

In the depicted embodiment, a client 164 of the MLS may submit a modelexecution request 812 to the MLS control plane 180 via a programmaticinterface 861. The model execution request may specify the executionmode (batch, online or local), the input data to be used for the modelrun (which may be produced using a specified data source or recipe insome cases), the type of output (e.g., a prediction or an evaluation)that is desired, and/or optional parameters (such as desired modelquality targets, minimum input record group sizes to be used for onlinepredictions, and so on). In response the MLS may generate a plan formodel execution and select the appropriate resources to implement theplan. In at least some embodiments, a job object may be generated uponreceiving the execution request 812 as described earlier, indicating anydependencies on other jobs (such as the execution of a recipe forfeature processing), and the job may be placed in a queue. For batchmode 865, for example, one or more servers may be identified to run themodel. For online mode 867, the model may be mounted (e.g., configuredwith a network address) to which data records may be streamed, and fromwhich results including predictions 868 and/or evaluations 869 can beretrieved. In at least one embodiment, clients may optionally specifyexpected workload levels for a model that is to be instantiated inonline mode, and the set of provider network resources to be deployedfor the model may be selected in accordance with the expected workloadlevel. For example, a client may indicate via a parameter of the modelexecution/creation request that up to 100 prediction requests per dayare expected on data sets of 1 million records each, and the serversselected for the model may be chosen to handle the specified requestrate. For local mode, the MLS may package up an executable local version843 of the model (where the details of the type of executable that is tobe provided, such as the type of byte code or the hardware architectureon which the model is to be run, may have been specified in theexecution request 812) and transmit the local model to the client. Insome embodiments, only a subset of the execution modes illustrated maybe supported. In some implementations, not all of the combinations ofexecution modes and output types may be supported—for example, whilepredictions may be supported for online mode in one implementation,evaluations may not be supported for online mode.

Methods for Implementing MLS Operations

FIGS. 9a and 9b are flow diagrams illustrating aspects of operationsthat may be performed at a machine learning service that supportsasynchronous scheduling of machine learning jobs, according to at leastsome embodiments. As shown in element 901 of FIG. 9a , the MLS mayreceive a request from a client via a programmatic interface (such as anAPI, a command-line tool, a web page, or a custom GUI) to perform aparticular operation on an entity belonging to a set of supported entitytypes of the MLS. The entity types may include, for example, datasources, statistics, feature processing recipes, models, aliases,predictions, and/or evaluations in the depicted embodiment. Theoperations requested may include, for example, create, read (or describethe attributes of), modify/update attributes, execute, search, or deleteoperations. Not all the operation types may apply to all the entitytypes in some embodiments—e.g., it may not be possible to “execute” adata source. In at least some implementations, the request may beencrypted or encapsulated by the client, and the MLS may have to extractthe contents of the request using the appropriate keys and/orcertificates.

The request may next be validated in accordance with various rules orpolicies of the MLS (element 904). For example, in accordance with asecurity policy, the permissions, roles or capabilities granted to therequesting client may be checked to ensure that the client is authorizedto have the requested operations performed. The syntax of the requestitself, and/or objects such as recipes passed as request parameters maybe checked for some types of requests. In some cases, the types of oneor more data variables indicated in the request may have to be checkedas well.

If the request passes the validation checks, a decision may be made asto whether a job object is to be created for the request. As mentionedearlier, in some cases, the amount of work required may be small enoughthat the MLS may simply be able to perform the requested operationsynchronously or “in-line”, instead of creating and inserting a jobobject into a queue for asynchronous execution (at least in scenarios inwhich the prerequisites or dependencies of the request have already beenmet, and sufficient resources are available for the MLS to complete therequested work). If an analysis of the request indicates that a job isrequired (as detected in element 907), a job object may be generated,indicating the nature of the lower-level operations to be performed atthe MLS as well as any dependencies on other jobs, and the job objectmay be placed in a queue (element 913). In some implementations, therequesting client may be notified that the request has been accepted forexecution (e.g., by indicating to the client that a job has been queuedfor later execution). The client may submit another programmatic requestwithout waiting for the queued job to be completed (or even begun) insome cases. If the job does not have any dependencies that have yet tobe met, and meets other criteria for immediate or in-line execution (asalso determined in element 907), the requested operation may beperformed without creating a job object (element 910) and the resultsmay optionally be provided to the requesting client. Operationscorresponding to elements 901-913 may be performed for each request thatis received via the MLS programmatic interface. At some point after aparticular job Jk is placed in the queue, Jk may be identified (e.g., bya job scheduler component of the MLS control plane) as the next job tobe implemented (element 951 of FIG. 9b ). To identify the next job to beimplemented, the scheduler may, for example, start from the head of thequeue (the earliest-inserted job that has not yet been executed) andsearch for jobs whose dependencies (if any are specified) have been met.

In addition to the kinds of validation indicated in element 904 of FIG.9a , the MLS may perform validations at various other stages in someembodiments, e.g., with the general goals of (a) informing clients assoon as possible when a particular request is found to be invalid, and(b) avoiding wastage of MLS resources on requests that are unlikely tosucceed. As shown in element 952 of FIG. 9b , one or more types ofvalidation checks may be performed on the job Jk identified in element951. For example, in one embodiment each client may have a quota orlimit on the resources that can be applied to their jobs (such as amaximum number of servers that can be used concurrently for all of agiven customer's jobs, or for any given job of the customer). In someimplementations respective quotas may be set for each of severaldifferent resource types—e.g., CPUs/cores, memory, disk, networkbandwidth and the like. In such scenarios, the job scheduler may beresponsible for verifying that the quota or quotas of the client onwhose behalf the job Jk is to be run have not been exhausted. If a quotahas been exhausted, the job's execution may be deferred until at leastsome of the client's resources are released (e.g., as a result of acompletion of other jobs performed on the same client's behalf). Suchconstraint limits may be helpful in limiting the ability of any givenclient to monopolize shared MLS resources, and also in minimizing thenegative consequences of inadvertent errors or malicious code. Inaddition to quota checks, other types of run-time validations may berequired for at least some jobs—e.g., data type checking may have to beperformed on the input data set for jobs that involve featureprocessing, or the MLS may have to verify that the input data set sizeis within acceptable bounds. Thus, client requests may be validatedsynchronously (at the time the request is received, as indicated inelement 904 of FIG. 9a ) as well as asynchronously (as indicated inelement 952 of FIG. 9b ) in at least some embodiments. A workloaddistribution strategy and processing plan may be identified for Jk—e.g.,the number of processing passes or phases to be used, the degree ofparallelism to be used, an iterative convergence criterion to be usedfor completing Jk (element 954). A number of additional factors may betaken into account when generating the processing plan in someembodiments, such as client budget constraints (if any), the datadurability needs of the client, the performance goals of the client,security needs (such as the need to run third-party code orclient-provided code in isolation instead of in multi-tenant mode).

In accordance with the selected distribution strategy and processingplan, a set of resources may be identified for Jk (element 957). Theresources (which may include compute servers or clusters, storagedevices, and the like) may be selected from the MLS-managed sharedpools, for example, and/or from customer-assigned or customer-ownedpools. JK's operations may then be performed on the identified resources(element 960), and the client on whose behalf Jk was created mayoptionally be notified when the operations complete (or in the event ofa failure that prevents completion of the operations).

Idempotent Programmatic Interfaces

Some of the types of operations requested by MLS clients may beresource-intensive. For example, ingesting a terabyte-scale data set(e.g., in response to a client request to create a data store) orgenerating statistics on such a data set may take hours or days,depending on the set of resources deployed and the extent of parallelismused. Given the asynchronous manner in which client requests are handledin at least some embodiments, clients may sometimes end up submittingthe same request multiple times. In some cases, such multiplesubmissions may occur because the client is unaware whether the previoussubmission was accepted or not (e.g., because the client failed tonotice an indication that the previous submission was accepted, orbecause such an indication was lost). In other cases, a duplicaterequest may be received because the client has assumed that since theexpected results of completing the requested task have not been providedfor a long time, the previous request must have failed. If, in responseto such a duplicate submission, the MLS actually schedules anotherpotentially large job, resources may be deployed unnecessarily and theclient may in some cases be billed twice for a request that was onlyintended to be serviced once. Accordingly, in order to avoid suchproblematic scenarios, in at least one embodiment one or more of theprogrammatic interfaces supported by the MLS may be designed to beidempotent, such that the re-submission of a duplicate request by thesame client does not have negative consequences.

FIG. 10a is a flow diagram illustrating aspects of operations that maybe performed at a machine learning service at which a set of idempotentprogrammatic interfaces are supported, according to at least someembodiments. In FIG. 10a , a creation interface (e.g., an API similar to“createDataSource” or “createModel”) is used as an example of anidempotent programmatic interface. Although idempotency may beespecially useful for programmatic interfaces that involve creation ofartifacts such as data sources and models, idempotent interfaces mayalso be supported for other types of operations (e.g., deletes orexecutes) in various embodiments. As shown in element 1001, a request tocreate a new instance of an entity type ET1 may be received from aclient C1 at the MLS via a programmatic interface such as a particularAPI. The request may indicate an identifier ID1, selected by the client,which is to be used for the new instance. In some implementations, theclient may be required to specify the instance identifier, and theidentifier may be used as described below to detect duplicate requests.(Allowing the client to select the identifier may have the additionaladvantage that a client may be able to assign a more meaningful name toentity instances than a name assigned by the MLS.) The MLS may generatea representation IPR1 of the input parameters included in the client'sinvocation of the programmatic interface (element 1004). For example,the set of input parameters may be supplied as input to a selected hashfunction, and the output of the hash function may be saved as IPR1.

In the embodiment depicted in FIG. 10a , for at least some of theartifacts generated, the MLS repository may store the correspondinginstance identifier, input parameter representation, and clientidentifier (i.e., the identifier of the client that requested thecreation of the artifact). The MLS may check, e.g., via a lookup in theartifact repository, whether an instance of entity type ET1, withinstance identifier ID1 and client identifier C1 already exists in therepository. If no such instance is found (as detected in element 1007),a new instance of type ET1 with the identifier ID1, input parameterrepresentation IPR1 and client identifier C1 may be inserted into therepository (element 1007). In addition, depending on the type of theinstance, a job object may be added to a job queue to perform additionaloperations corresponding to the client request, such asreading/ingesting a data set, generating a set of statistics, performingfeature processing, executing a model, etc. A success response to theclient's request (element 1016) may be generated in the depictedembodiment. (It is noted that the success response may be implicit insome implementations—e.g., the absence of an error message may serve asan implicit indicator of success.)

If, in operations corresponding to element 1007, a pre-existing instancewith the same instance identifier ID1 and client identifier C1 is foundin the repository, the MLS may check whether the input parameterrepresentation of the pre-existing instance also matches IPR1 (element1013). If the input parameter representations also match, the MLS mayassume that the client's request is a (harmless) duplicate, and no newwork needs to be performed. Accordingly, the MLS may also indicatesuccess to the client (either explicitly or implicitly) if such aduplicate request is found (element 1016). Thus, if the client hadinadvertently resubmitted the same request, the creation of a new jobobject and the associated resource usage may be avoided. In someimplementations, if the client request is found to be an exact duplicateof an earlier request using the methodology described, an indication maybe provided to the client that the request, while not being designatedas an error, was in fact identified as a duplicate. If the inputparameter representation of the pre-existing instance does not matchthat of the client's request, an error message may be returned to theclient (element 1019), e.g., indicating that there is a pre-existinginstance of the same entity type ET1 with the same identifier. In someimplementations, instead of requiring the client to submit anidentifier, a different approach to duplicate detection may be used,such as the use of a persistent log of client requests, or the use of asignature representing the (request, client) combination.

Best Practices

One of the advantages of building a machine learning service that may beused by large numbers of customers for a variety of use cases is that itmay become possible over time to identify best practices, e.g., withrespect to which techniques work best for data cleansing, sampling orsub-set extraction, feature processing, predicting, and so on. FIG. 10bis a flow diagram illustrating aspects of operations that may beperformed at a machine learning service to collect and disseminateinformation about best practices related to different problem domains,according to at least some embodiments. As shown in element 1051, atleast some of the artifacts (such as recipes and models) generated atthe MLS as a result of client requests may be classified into groupsbased on problem domains—e.g., some artifacts may be used for financialanalysis, others for computer vision applications, others forbioinformatics, and so on. Such classification may be performed based onvarious factors in different embodiments—e.g. based on the types ofalgorithms used, the names of input and output variables,customer-provided information, the identities of the customers, and soon.

In some embodiments, the MLS control plane may comprise a set ofmonitoring agents that collect performance and other metrics from theresources used for the various phases of machine learning operations(element 1054). For example, the amount of processing time it takes tobuild N trees of a random forest using a server with a CPU rating of C1and a memory size of M1 may be collected as a metric, or the amount oftime it takes to compute a set of statistics as a function of the numberof data attributes examined from a data source at a database service maybe collected as a metric. The MLS may also collect ratings/rankings orother types of feedback from MLS clients regarding the effectiveness orquality of various approaches or models for the different problemdomains. In some embodiments, quantitative measures of model predictiveeffectiveness such as the area under receiver operating characteristic(ROC) curves for various classifiers may also be collected. In oneembodiment, some of the information regarding quality may be deduced orobserved implicitly by the MLS instead of being obtained via explicitclient feedback, e.g., by keeping track of the set of parameters thatare changed during training iterations before a model is finally usedfor a test data set. In some embodiments, clients may be able to decidewhether their interactions with the MLS can be used for best practiceknowledge base enhancement or not—e.g., some clients may not wish theircustomized techniques to become widely used by others, and may thereforeopt out of sharing metrics associated with such techniques with the MLSor with other users.

Based on the collected metrics and/or feedback, respective sets of bestpractices for various phases of machine learning workflows may beidentified (element 1057). Some of the best practices may be specific toparticular problem domains, while others may be more generallyapplicable, and may therefore be used across problem domains.Representations or summaries of the best practices identified may bestored in a knowledge base of the MLS. Access (e.g., via a browser or asearch tool) to the knowledge base may be provided to MLS users (element1060). The MLS may also incorporate the best practices into theprogrammatic interfaces exposed to users—e.g., by introducing new APIsthat are more likely to lead users to utilize best practices, byselecting default parameters based on best practices, by changing theorder in which parameter choices in a drop-down menu are presented sothat the choices associated with best practices become more likely to beselected, and so on. In some embodiments the MLS may provide a varietyof tools and/or templates that can help clients to achieve their machinelearning goals. For example, a web-based rich text editor or installableintegrated development environment (IDE) may be provided by the MLS,which provides templates and development guidance such as automatedsyntax error correction for recipes, models and the like. In at leastone embodiment, the MLS may provide users with candidate models orexamples that have proved useful in the past (e.g., for other clientssolving similar problems). The MLS may also maintain a history of theoperations performed by a client (or by a set of users associated withthe same customer account) across multiple interaction sessions in someimplementations, enabling a client to easily experiment with or employartifacts that the same client generated earlier.

Feature Processing Recipes

FIG. 11 illustrates examples interactions associated with the use ofrecipes for data transformations at a machine learning service,according to at least some embodiments. In the depicted embodiment, arecipe language defined by the MLS enables users to easily and conciselyspecify transformations to be performed on specified sets of datarecords to prepare the records for use for model training andprediction. The recipe language may enable users to create customizedgroups of variables to which one or more transformations are to beapplied, define intermediate variables and dependencies upon otherartifacts, and so on, as described below in further detail. In oneexample usage flow, raw data records may first be extracted from a datasource (e.g., by input record handlers such as those shown in FIG. 1with the help of an MLS I/O library), processed in accordance with oneor more recipes, and then used as input for training or prediction. Inanother usage flow, the recipe may itself incorporate the trainingand/or prediction steps (e.g., a destination model or models may bespecified within the recipe). Recipes may be applied either to datarecords that have already split into training and test subsets, or tothe entire data set prior to splitting into training and test subsets. Agiven recipe may be re-used on several different data sets, potentiallyfor a variety of different machine learning problem domains, in at leastsome embodiments. The recipe management components of the MLS may enablethe generation of easy-to-understand compound models (in which theoutput of one model may be used as the input for another, or in whichiterative predictions can be performed) as well as the sharing andre-use of best practices for data transformations. In at least oneembodiment, a pipeline of successive transformations to be performedstarting with a given input data set may be indicated within a singlerecipe. In one embodiment, the MLS may perform parameter optimizationfor one or more recipes—e.g., the MLS may automatically vary suchtransformation properties as the sizes of quantile bins or the number ofroot words to be included in an n-gram in an attempt to identify a moreuseful set of independent variables to be used for a particular machinelearning algorithm.

In some embodiments, a text version 1101 of a transformation recipe maybe passed as a parameter in a “createRecipe” MLS API call by a client.As shown, a recipe validator 1104 may check the text version 1101 of therecipe for lexical correctness, e.g., to ensure that it complies with agrammar 1151 defined by the MLS in the depicted embodiment, and that therecipe comprises one or more sections arranged in a predefined order (anexample of the expected structure of a recipe is illustrated in FIG. 12and described below). In at least some embodiments, the version of therecipe received by the MLS need not necessarily be a text version;instead, for example, a pre-processed or partially-combined version(which may in some cases be in a binary format rather than in plaintext) may be provided by the client. In one embodiment, the MLS mayprovide a tool that can be used to prepare recipes—e.g., in the form ofa web-based recipe editing tool or a downloadable integrated developmentenvironment (IDE). Such a recipe preparation tool may, for example,provide syntax and/or parameter selection guidance, correct syntaxerrors automatically, and/or perform at least some level ofpre-processing on the recipe text on the client side before the recipe(either in text form or binary form) is sent to the MLS service. Therecipe may use a number of different transformation functions or methodsdefined in one or more libraries 1152, such as functions to formCartesian products of variables, n-grams (for text data), quantile bins(for numeric data variables), and the like. The libraries used forrecipe validation may include third-party or client-provided functionsor libraries in at least some embodiments, representing custom featureprocessing extensions that have been incorporated into the MLS toenhance the service's core or natively-supported feature processingcapabilities. The recipe validator 1104 may also be responsible forverifying that the functions invoked in the text version 1101 are (a)among the supported functions of the library 1152 and (b) used with theappropriate signatures (e.g., that the input parameters of the functionsmatch the types and sequences of the parameters specified in thelibrary). In some embodiments, MLS customers may register additionalfunctions as part of the library, e.g., so that custom “user-definedfunctions” (UDFs) can also be included in the recipes. Customers thatwish to utilize UDFs may be required to provide an indication of amodule that can be used to implement the UDFs (e.g., in the form ofsource code, executable code, or a reference to a third-party entityfrom which the source or executable versions of the module can beobtained by the MLS) in some embodiments. A number of differentprogramming languages and/or execution environments may be supported forUDFs in some implementations, e.g., including Java™, Python, and thelike. The text version of the recipe may be converted into an executableversion 1107 in the depicted embodiment. The recipe validator 1104 maybe considered analogous to a compiler for the recipe language, with thetext version of the recipe analogous to source code and the executableversion analogous to the compiled binary or byte code derived from thesource code. The executable version may also be referred to as a featureprocessing plan in some embodiments. In the depicted embodiment, boththe text version 1101 and the executable version 1107 of a recipe may bestored within the MLS artifact repository 120.

A run-time recipe manager 1110 of the MLS may be responsible for thescheduling of recipe executions in some embodiments, e.g., in responseto the equivalent of an “executeRecipe” API specifying an input dataset. In the depicted embodiment, two execution requests 1171A and 1171Bfor the same recipe R1 are shown, with respective input data sets IDS1and IDS2. The input data sets may comprise data records whose variablesmay include instances of any of a variety of data types, such as, forexample text, a numeric data type (e.g., real or integer), Boolean, abinary data type, a categorical data type, an image processing datatype, an audio processing data type, a bioinformatics data type, astructured data type such as a particular data type compliant with theUnstructured Information Management Architecture (UIMA), and so on. Ineach case, the run-time recipe manager 1110 may retrieve (or generate)the executable version of R1, perform a set of run-time validations(e.g., to ensure that the requester is permitted to execute the recipe,that the input data appears to be in the correct or expected format, andso on), and eventually schedule the execution of the transformationoperations of R1 at respective resource sets 1175A and 1175B. In atleast some cases, the specific libraries or functions to be used for thetransformation may be selected based on the data types of the inputrecords—e.g., instances of a particular structured data type may have tobe handled using functions or methods of a corresponding library definedfor that data type. Respective outputs 1185A and 1185B may be producedby the application of the recipe R1 on IDS1 and IDS2 in the depictedembodiment. Depending on the details of the recipe R1, the outputs 1185Amay represent either data that is to be used as input for a model, or aresult of a model (such as a prediction or evaluation). In at least someembodiments, a recipe may be applied asynchronously with respect to theexecution request—e.g., as described earlier, a job object may beinserted into a job queue in response to the execution request, and theexecution may be scheduled later. The execution of a recipe may bedependent on other jobs in some cases—e.g., upon the completion of jobsassociated with input record handling (decryption, decompression,splitting of the data set into training and test sets, etc.). In someembodiments, the validation and/or compilation of a text recipe may alsoor instead be managed using asynchronously-scheduled jobs.

In some embodiments, a client request that specifies a recipe in textformat and also includes a request to execute the recipe on a specifieddata set may be received—that is, the static analysis steps and theexecution steps shown in FIG. 11 may not necessarily require separateclient requests. In at least some embodiments, a client may simplyindicate an existing recipe to be executed on a data set, selected forexample from a recipe collection exposed programmatically by the MLS,and may not even have to generate a text version of a recipe. In oneembodiment, the recipe management components of the MLS may examine theset of input data variables, and/or the outputs of the transformationsindicated in a recipe, automatically identify groups of variables oroutputs that may have a higher predictive capability than others, andprovide an indication of such groups to the client.

FIG. 12 illustrates example sections of a recipe, according to at leastsome embodiments. In the depicted embodiment, the text of a recipe 1200may comprise four separate sections—a group definitions section 1201, anassignments section 1204, a dependencies section 1207, and anoutput/destination section 1210. In some implementations, only theoutput/destination section may be mandatory; in other implementations,other combinations of the sections may also or instead be mandatory. Inat least one embodiment, if more than one of the four section typesshown in FIG. 12 is included in a recipe, the sections may have to bearranged in a specified order. In at least one embodiment, a destinationmodel (i.e., a machine learning model to which the output of the recipetransformations is to be provided) may be indicated in a separatesection than the output section.

In the group definitions section 1201, as implied by the name, clientsmay define groups of input data variables, e.g., to make it easier toindicate further on in the recipe that the same transformation operationis to be applied to all the member variables of a group. In at leastsome embodiments, the recipe language may define a set of baselinegroups, such as ALL_INPUT (comprising all the variables in the inputdata set), ALL_TEXT (all the text variables in the data set),ALL_NUMERIC (all integer and real valued variables in the data set),ALL_CATEGORICAL (all the categorical variables in the data set) andALL_BOOLEAN (all the Boolean variables in the data set, e.g., variablesthat can only have the values “true” or “false” (which may berepresented as “1” and “0” respectively in some implementations)). Insome embodiments, the recipe language may allow users to change or“cast” the types of some variables when defining groups—e.g., variablesthat appear to comprise arbitrary text but are only expected to haveonly a discrete set of values, such as the names of the months of theyear, the days of the week, or the states of a country, may be convertedto categorical variables instead of being treated as generic textvariables. Within the group definitions section, the methods/functions“group” and “group_remove” (or other similar functions representing setoperations) may be used to combine or exclude variables when definingnew groups. A given group definition may refer to another groupdefinition in at least some embodiments. In the example section contents1250 shown in FIG. 12, three groups are defined: LONGTEXT, SPECIAL_TEXTand BOOLCAT. LONGTEXT comprises all the text variables in the inputdata, except for variables called “title” and “subject”. SPECIAL_TEXTincludes the text variables “subject” and “title”. BOOLCAT includes allthe Boolean and categorical variables in the input data. It is notedthat at least in some embodiments, the example group definitions shownmay be applied to any data set, even if the data set does not contain a“subject” variable, a “title” variable, any Boolean variables, anycategorical variables, or even any text variables. If there are no textvariables in an input data set, for example, both LONGTEXT andSPECIAL_TEXT would be empty groups with no members with respect to thatparticular input data set in such an embodiment.

Intermediate variables that may be referenced in other sections of therecipe 1200 may be defined in the assignments section 1204. In theexample assignments section, a variable called “binage” is defined interms of a “quantile_bin” function (which is assumed to be includedamong the pre-defined library functions of the recipe language in thedepicted embodiment) applied to an “age” variable in the input data,with a bin count of “30”. A variable called “countrygender” is definedas a Cartesian product of two other variables “country” and “gender” ofthe input data set, with the “cartesian” function assumed to be part ofthe pre-defined library. In the dependencies section 1207, a user mayindicate other artifacts (such as the model referenced as “clustermodel”in the illustrated example, with the MLS artifact identifier“pr-23872-28347-alksdjf”) upon which the recipe depends. For example, insome cases, the output of a model that is referenced in the dependenciessection of the recipe may be ingested as the input of the recipe, or aportion of the output of the referenced model may be included in theoutput of the recipe. The dependencies section may, for example, be usedby the MLS job scheduler when scheduling recipe-based jobs in thedepicted embodiment. Dependencies on any of a variety of artifacts maybe indicated in a given recipe in different embodiments, including otherrecipes, aliases, statistics sets, and so on.

In the example output section 1210, a number of transformations areapplied to input data variables, groups of variables, intermediatevariables defined in earlier sections of the recipe, or the output of anartifact identified in the dependencies section. The transformed data isprovided as input to a different model identified as “model1”. Aterm-frequency-inverse document frequency (tfidf) statistic is obtainedfor the variables included in the LONGTEXT group, after punctuation isremoved (via the “nopunct” function) and the text of the variables isconverted to lowercase (by the “lowercase” function). The tfidf measuremay be intended to reflect the relative importance of words within adocument in a collection or corpus; the tfidf value for a given wordtypically is proportional to the number of occurrences of the word in adocument, offset by the frequency of the word in the collection as awhole. The tfidf, nopunct and lowercase functions are all assumed to bedefined in the recipe language's library. Similarly, othertransformations indicated in the output section use the osb (orthogonalsparse bigrams) library function, the quantile_bin library function forbinning or grouping numeric values, and the Cartesian product function.Some of the outputs indicated in section 1210 may not necessarilyinvolve transformations per se: e.g., the BOOLCAT group's variables inthe input data set may simply be included in the output, and the“clusterNum” output variable of “clustermodel” may be included withoutany change in the output of the recipe as well.

In at least some embodiments, the entries listed in the output sectionmay be used to implicitly discard those input data variables that arenot listed. Thus, for example, if the input data set includes a“taxable-income” numeric variable, it may simply be discarded in theillustrated example since it is not directly or indirectly referred toin the output section. The recipe syntax and section-by-sectionorganization shown in FIG. 12 may differ from those of otherembodiments. A wide variety of functions and transformation types (atleast some of which may differ from the specific examples shown in FIG.12) may be supported in different embodiments. For example, date/timerelated functions “dayofweek”, “hourofday” “month”, etc. may besupported in the recipe language in some embodiments. Mathematicalfunctions such as “sqrt” (square root), “log” (logarithm) and the likemay be supported in at least one embodiment. Functions to normalizenumeric values (e.g., map values from a range {−N1 to +N2} into a range{0 to 1}), or to fill in missing values (e.g.,“replace_missing_with_mean(ALL_NUMERIC)”) may be supported in someembodiments. Multiple references within a single expression to one ormore previously-defined group variables, intermediate variables, ordependencies may be allowed in one embodiment: e.g., the recipe fragment“replace_missing(ALL_NUMERIC, mean(ALL_NUMERIC))” may be consideredvalid. Mathematical expressions involving combinations of variables suchas “‘income’+10*‘capital_gains’” may also be permitted within recipes inat least some embodiments. Comments may be indicated by delimiters suchas “//” in some recipes.

Recipe Validation

FIG. 13 illustrates an example grammar that may be used to defineacceptable recipe syntax, according to at least some embodiments. Thegrammar shown may be formatted in accordance with the requirements of aparser generator such as a version of ANTLR (ANother Tool for LanguageRecognition). As shown, the grammar 1320 defines rules for the syntax ofexpressions used within a recipe. Given a grammar similar to that shownin FIG. 13, a tools such as ANTLR may generate a parser than can buildan abstract syntax tree from a text version of a recipe, and theabstract syntax tree may then be converted into a processing plan by theMLS control plane. An example tree generated using the grammar 1320 isshown in FIG. 14.

In the example grammar “MLS-Recipe” shown in FIG. 13, an expression“expr” can be one of a “BAREID”, a “QUOTEDID”, a “NUMBER” or a“functioncall”, with each of the latter four entities defined furtherdown in the grammar. A BAREID starts with an upper case or lower caseletter and can include numerals. A QUOTEDID can comprise any text withinsingle quotes. NUMBERs comprise real numeric values with or withoutexponents, as well as integers. A functioncall must include a functionname (a BAREID) followed by zero or more parameters within roundbrackets. Whitespace and comments are ignored when generating anabstract syntax tree in accordance with the grammar 1320, as indicatedby the lines ending in “→skip”.

FIG. 14 illustrates an example of an abstract syntax tree that may begenerated for a portion of a recipe, according to at least someembodiments. The example recipe fragment 1410 comprising the text“cartesian(binage, quantile_bin(‘hours-per-week’, 10))” may betranslated into abstract syntax tree 1420 in accordance with grammar1320 (or some other similar grammar) in the depicted embodiment. Asshown, “cartesian” and “quantile_bin” are recognized as function calls,each with two parameters. During the syntax analysis of the illustratedrecipe fragment, recipe validator 1104 may ensure that the number andorder of the parameters passed to “cartesian” and “quantile_bin” matchthe definitions of those functions, and that the variables “binage” and“hours_per_week” are defined within the recipe. If any of theseconditions are not met, an error message indicating the line numberwithin the recipe at which the “cartesian” fragment is located may beprovided to the client that submitted the recipe. Assuming that novalidation errors are found in the recipe as a whole, an executableversion of the recipe may be generated, of which a portion 1430 mayrepresent the fragment 1410.

Domain-Specific Recipe Collections

In at least some embodiments, some users of the MLS may not be expertsat feature processing, at least during a period when they start usingthe MLS. Accordingly, the MLS may provide users with access to acollection of recipes that have previously been found to be useful invarious problem domains. FIG. 15 illustrates an example of aprogrammatic interface that may be used to search for domain-specificrecipes available from a machine learning service, according to at leastsome embodiments. As shown, a web page 1501 may be implemented for arecipe search, which includes a message area 1504 providing high-levelguidance to MLS users, and a number of problem domains for which recipesare available. In the depicted example, a MLS customer can use acheck-box to select from among the problem domains fraud detection 1507,sentiment analysis 1509, image analysis 1511, genome analysis 1513, orvoice recognition 1515. A user may also search for recipes associatedwith other problem domains using search term text block 1517 in thedepicted web page.

For the selected problem domain (image analysis), links to five examplerecipes are shown on web page 1501: recipes FR1 and FR2 for facialrecognition, BTR1 for brain tumor recognition, ODA1 for ocean debrisrecognition, and AED1 for astronomical event detection. Additionaldetails regarding a given recipe may be obtained by the user by clickingon the recipe's name: for example, in some embodiments, a description ofwhat the recipe does may be provided, ratings/rankings of the recipesubmitted by other users may be provided, comments submitted by otherusers on the recipes, and so on. If a user finds a recipe that they wishto use (either unchanged or after modifying the recipe), they may beable to download the text version of the recipe, e.g., for inclusion ina subsequent MLS API invocation. As indicated in the message area 1504,users may also be able to submit their own recipes for inclusion in thecollection exposed by the MLS in the depicted embodiment. In at leastsome implementations, the MLS may perform some set of validation stepson a submitted recipe (e.g., by checking that the recipe producesmeaningful output for various input data sets) before allowing otherusers access.

Automated Parameter Tuning for Recipe Transformations

For many types of feature processing transformation operations, such ascreating quantile bins for numeric data attributes, generating ngrams,or removing sparse or infrequent words from documents being analyzed,parameters may typically have to be selected, such as thesizes/boundaries of the bins, the lengths of the ngrams, the removalcriteria for sparse words, and so on. The values of such parameters(which may also be referred to as hyper-parameters in some environments)may have a significant impact on the predictions that are made using therecipe outputs. Instead of requiring MLS users to manually submitrequests for each parameter setting or each combination of parametersettings, in some embodiments the MLS may support automated parameterexploration. FIG. 16 illustrates an example of a machine learningservice that automatically explores a range of parameter settings forrecipe transformations on behalf of a client, and selects acceptable orrecommended parameter settings based on results of such explorations,according to at least some embodiments.

In the depicted embodiment, an MLS client 164 may submit a recipeexecution request 1601 that includes parameter auto-tune settings 1606.For example, the client 164 may indicate that the bin sizes/boundariesfor quantile binning of one or more variables in the input data shouldbe chosen by the service, or that the number of words in an n-gramshould be chosen by the service. Parameter exploration and/orauto-tuning may be requested for various clustering-related parametersin some embodiments, such as the number of clusters into which a givendata set should be classified, the cluster boundary thresholds (e.g.,how far apart two geographical locations can be to be considered part ofa set of “nearby” locations), and so on. Various types of imageprocessing parameter settings may be candidates for automated tuning insome embodiments, such as the extent to which a given image should becropped, rotated, or scaled during feature processing. Automatedparameter exploration may also be used for selection dimensionalityvalues for a vector representation of a text document (e.g., inaccordance with the Latent Dirichlet Allocation (LDA) technique) orother natural language processing techniques. In some cases, the clientmay also indicate the criteria to be used to terminate exploration ofthe parameter value space, e.g., to arrive at acceptable parametervalues. In at least some embodiments, the client may be given the optionof letting the MLS decide the acceptance criteria to be used—such anoption may be particularly useful for non-expert users. In oneimplementation, the client may indicate limits on resources or executiontime for parameter exploration. In at least one implementation, thedefault setting for an auto-tune setting for at least some outputtransformations may be “true”, e.g., a client may have to explicitlyindicate that auto-tuning is not to be performed in order to prevent theMLS from exploring the parameter space for the transformations.

In response to a determination that auto-tuning is to be performed for agiven transformation operation, the MLS (e.g., a parameter explorer 1642of the recipe run-time manager 1640) may select a parameter tuning range1654 for the transformation (e.g., whether the quantile bin counts of10, 20, 30 and 40 should be explored for a particular numeric variable).The parameter ranges may be selected based on a variety of factors indifferent embodiments, including best practices known to the MLS forsimilar transformations, resource constraints, the size of the inputdata set, and so on. In scenarios in which respective parameters forcombinations of several transformation operations are to be tuned (e.g.,if quantile binning is being auto-tuned for more than one variable), theparameter explorer 1642 may select a respective set of values for eachparameter so as to keep the number of combinations that are to be triedbelow a threshold. Having determined the range of parameter values, theparameter explorer may execute iterations of transformations for eachparameter value or combination, storing the iteration results 1656 in atleast some implementations in temporary storage. Based on the resultsets generated for the different parameter values and the optimizationcriteria being used, at least one parameter value may be identified asacceptable for each parameter. In the depicted embodiment, a resultsnotification 1667 may be provided to the client, indicating the acceptedor recommended parameter value or values 1668 for the differentparameters being auto-tuned. For some parameters, it may not always bestraightforward to identify a particular parameter value as being thesingle best value, e.g., because several different values may lead tosimilar results. In some embodiments, instead of identifying a singleoptimal value for such a parameter, the MLS may instead identify a setof candidate values {V1, V2, V3, . . . , Vn} for a given parameter P,such that all the values of the set provide results of similar quality.The set of candidate values may be provided to the client, enabling theclient to choose the specific parameter value to be used, and the clientmay notify the MLS regarding the selected parameter value. In oneembodiment, the client may only be provided with an indication of theresults of the recipe transformations obtained using theaccepted/optimized parameter values, without necessarily being informedabout the parameter value settings used.

Methods of Supporting Feature Processing Via Re-Usable Recipes

FIG. 17 is a flow diagram illustrating aspects of operations that may beperformed at a machine learning service that supports re-usable recipesfor data set transformations, according to at least some embodiments. Asshown in element 1701, an indication of a text version of a recipe fortransformation operations to be performed on input data sets may bereceived at a network-accessible MLS implemented at a provider network.In one embodiment, the recipe text may include one or more of foursections in accordance with a recipe language defined by the MLS: agroup definitions section, an assignment section, a dependency section,and an output/destination section (which may also be referred to simplyas the output section). In some embodiments, one or more sections (suchas the output section) may be mandatory. In general, theoutput/destination section may indicate various feature processingtransformation operations that are to be performed on entities definedin other sections of the recipe, or directly on input variables of adata set. The group definitions section may be used to define customgroups of input variables (or input data variables combined with othergroups, or groups derived from other groups). Such group definitions maymake it easier to specify in the output section that a commontransformation is to be applied to several variables. A number ofbuilt-in or predefined groups may be supported by the recipe language insome embodiments, such as ALL_NUMERIC or ALL_CATEGORICAL, along withfunctions such as “group_remove” and “group” to allow recipe creators toeasily indicate variable exclusions and combinations to be used whendefining new groups. The assignment section may be used to define one ormore intermediate variables that can be used elsewhere in the recipe.The dependency section may indicate that the recipe depends on anothermachine learning artifact (such as a model, or another recipe) or onmultiple other artifacts stored in an MLS's repository. In someembodiments, the output section may indicate not just the specifictransformations to be applied to specified input variables, definedgroups, intermediate variables or output of the artifacts indicated inthe dependency section, but also the destination models to which thetransformation results are to be provided as input.

The machine learning service may natively support libraries comprising avariety of different transformation operations that can be used in therecipe's output section, such as the types of functions illustrated inFIG. 12. In some embodiments, several different libraries, eachcorresponding to a given problem domain or to a respective class ofmachine learning algorithm, may be supported by the MLS. In addition, inone embodiment MLS customers may be able to register their own customfunctions (called “user-defined functions” or UDFs), third-partyfunctions, or libraries comprising multiple UDFs or third-partyfunctions with the MLS to extend the core feature processingcapabilities of the MLS. UDFs may be provided to the MLS by clients in avariety of different formats (e.g., including one or more text formatsand/or one or more binary formats) in some embodiments. A number ofdifferent programming or scripting languages may be supported for UDFsin such embodiments. An API for registering externally-producedtransformation functions or libraries with the MLS may be supported insome embodiments, e.g., enabling a client to indicate whether thenewly-registered functions are to be made accessible to other clients orrestricted for use by the submitting client. In one implementation, arecipe may comprise an import section in which one or more libraries(e.g., libraries other than a core or standard library of the MLS) whosefunctions are used in the recipe may be listed. In some implementations,the MLS may impose resource usage restrictions on at least someUDFs—e.g., to prevent runaway consumption of CPU time, memory, diskspace and the like, a maximum limit may be set on the time that a givenUDF can run. In this way, the negative consequences of executingpotentially error-prone UDFs (e.g., a UDF whose logic comprises aninfinite loop under certain conditions) may be limited. In at least someembodiments, the recipe text (or a file or URL from which the recipetext can be read) may be passed as a parameter in an API (such as a“createRecipe” API) invoked by an MLS client.

The recipe text may be validated at the MLS, e.g., in accordance with aset of syntax rules of a grammar and a set of libraries that definesupported transformation methods or functions (element 1704). If syntaxerrors or unresolvable tokens are identified during the text validationchecks, in at least some embodiments error messages that indicate theportion of the text that needs to be corrected (e.g., by indicating theline number and/or the error-inducing tokens) may be provided to therecipe submitter. If no errors are found, or after the errors found arecorrected and the recipe is re-submitted, an executable version of therecipe text may be generated (element 1707). One or both versions of therecipe (the text version and the executable version) may be stored in anartifact repository of the MLS in the depicted embodiment, e.g., with aunique recipe identifier generated by the MLS being provided to therecipe submitter.

The MLS may determine, e.g., in response to a different API invocationor because the initial submission of the recipe included an executionrequest, that the recipe is to be applied to a particular data set(element 1710). The data set may be checked to ensure that it meetsrun-time acceptance criteria, e.g., that the input variable names anddata types match those indicated in the recipe, and that the data set isof an acceptable size (element 1713). A set of provider networkresources (e.g., one or more compute servers, configured withappropriate amounts of storage and/or network capacity as determined bythe MLS) may be identified for the recipe execution (element 1716). Thetransformations indicated in the recipe may then be applied to the inputdata set (element 1719). In some embodiments, as described above withrespect to FIG. 16, the MLS may perform parameter explorations in aneffort to identify acceptable parameter values for one or more of thetransformations. After the recipe transformations are completed (and/orthe results of the transformations are provided to the appropriatedestinations, such as a model specified in the recipe itself), anotification that the recipe's execution is complete may be provided tothe client that requested the execution (element 1722) in the depictedembodiment.

I/O-Efficient Input Data Filtering Sequences

As mentioned earlier, some machine learning input data sets can be muchlarger (e.g., on the order of terabytes) than the amount of memory thatmay be available at any given server of a machine learning service. Inorder to train and evaluate a model, a number of filtering or inputrecord rearrangement operations may sometimes have to be performed in asequence on an input data set. For example, for cross-validating aclassification model, the same input data set may have to be split intotraining and test data sets multiple times, and such split operationsmay be considered one example of input filtering. Other input filteringoperation types may include sampling (obtaining a subset of the dataset), shuffling (rearranging the order of the input data objects), orpartitioning for parallelism (e.g., dividing a data set into N subsetsfor a computation implemented using map-reduce or a similar parallelcomputing paradigm, or for performing multiple parallel trainingoperations for a model). If a data set that takes up several terabytesof space were to be read from and/or written to persistent storage foreach filtering operation (such as successive shuffles or splits), thetime taken for just the I/O operations alone may become prohibitive,especially if a large fraction of the I/O comprised random reads ofindividual observation records of the input data set from rotatingdisk-based storage devices. Accordingly, in some embodiments, atechnique of mapping large data sets into smaller contiguous chunks thatare read once into some number of servers' memories, and then performingsequences of chunk-level filtering operations in place without copyingthe data set to persistent storage between successive filteringoperations may be implemented at a machine learning service. In at leastone such embodiment, an I/O library may be implemented by the machinelearning service, enabling a client to specify, via a single invocationof a data-source-agnostic API, a variety of input filtering operationsto be performed on a specified data set. Such a library may beespecially useful in scenarios in which the input data sets comprisevarying-length observation records stored in files within file systemdirectories rather than in structured database objects such as tables,although the chunking and in-memory filtering technique described belowmay in general be performed for any of a variety of data source types(including databases) as described below. The I/O library may allowclients to indicate data sources of various types (e.g., single-hostfile systems, distributed file systems, storage services of implementedat a provider network, non-relational databases, relational databases,and so on), and may be considered data-source-agnostic in that the sametypes of filtering operations may be supported regardless of the type ofdata source being used. In some cases, respective subsets of a giveninput data set may be stored in different types of data sources.

FIG. 18 illustrates an example procedure for performing efficientin-memory filtering operations on a large input data set by a machinelearning service (MLS), according to at least some embodiments. Asshown, a data source 1802 from which a client of the machine learningservice wishes to extract observation records may comprise a pluralityof data objects such as files F1, F2, F3 and F4 in the depictedembodiment. The sizes of the files may differ, and/or the number ofobservation records in any given file may differ from the number ofobservation records in other files. As used herein, the term“observation record” may be used synonymously with the term “datarecord” when referring to input data for machine learning operations. Adata record extraction request submitted by the client may indicate thedata source 1802, e.g., by referring to locations (e.g., a directoryname or a set of URLs) of files F1, F2, F3 and F4. In response to theextraction request, the MLS may ascertain or estimate the size of thedata set as a whole (e.g., the combined size of the files) in thedepicted embodiment, and determine an order in which the files should belogically concatenated to form a unified address space. In the exampleshown, data set 1804 may be generated, for example, by logicallyconcatenating the files in the order F1, F2, F3 and F4. In someembodiments, the client's data record extraction request may specify theorder in which the files of a multi-file data set are to be combined (atleast initially), and/or the sizes of the files. In other embodiments,the MLS may determine the concatenation order (e.g., based on anycombination of various factors such as lexical ordering of the filenames, the sizes of the files, and so on). It is noted that althoughfiles are used as an example of the data objects in which observationrecords are stored in FIG. 18 and some subsequent figures, similartechniques for input filtering may be used regardless of the type of thedata objects used (e.g., volumes providing a block-level interface,database records, etc.) in various embodiments.

The concatenated address space of data set 1804 may then be sub-dividedinto a plurality of contiguous chunks, as indicated in chunk mapping1806. The size of a chunk (Cs) may be determined based on any of severalfactors in different embodiments. For example, in one embodiment, thechunk size may be set such that each chunk can fit into the memory of anMLS server (e.g., a server of pools 185 of FIG. 1) at which at least aportion of the response to the client's data record extraction requestis to be generated. Consider a simple scenario in which the memoryportions available for the data records at each of several MLS serversis Sm. In such a scenario, a chunk size Cs such that Cs is less than orequal to Sm may be selected, as shown in FIG. 18. In other embodiments,the client request may indicate a chunk sizing preference, or the MLSmay define a default chunk size to be used even if different servershave different amounts of memory available for the data records. In someembodiments, the chunk size to be used for responding to one recordextraction request may differ from that used for another recordextraction request; in other embodiments, the same chunk size may beused for a plurality of requests, or for all requests. The sub-divisionof the concatenated data set 1804 into contiguous chunks (rather than,for example, randomly selected sub-portions) may increase the fractionof the data set that can be read in via more efficient sequential readsthan the fraction that has to be read via random reads, as illustratedbelow with respect to FIG. 19. In some embodiments, different chunks ofa given chunk mapping may have different sizes—e.g., chunk sizes neednot necessarily be identical for all the chunks of a given data set. Itis noted that the initial sub-division of the data set into chunksrepresents a logical operation that may be performed prior to physicalI/O operations on the data set.

In the depicted embodiment, an initial set of candidate chunk boundaries1808 may be determined, e.g., based on the chunk sizes being used. Asshown, candidate chunk boundaries need not be aligned with fileboundaries in at least some embodiments. The candidate chunk boundariesmay have to be modified somewhat to align chunk boundaries withobservation record boundaries in at least some embodiments when thechunks are eventually read, as described below in greater detail withreference to FIG. 22. A chunk-level filtering plan 1850 may be generatedfor the chunked data set 1810 in some embodiments, e.g., based oncontents of a filtering descriptor (which may also be referred to as aretrieval descriptor) included in the client's request. The chunk-levelfiltering plan may indicate, for example, the sequence in which aplurality of in-memory filtering operations 1870 (e.g., 1870A, 1870B and1870N) such as shuffles, splits, samples, or partitioning for parallelcomputations such as map reduce are to be performed on the chunks of theinput data. In some embodiments the machine learning model may supportparallelized training of models, in which for example respective (andpotentially partially overlapping) subsets of an input data set may beused to train a given model in parallel. The duration of one trainingoperation may overlap at least partly with the duration of another insuch a scenario, and the input data set may be partitioned for theparallel training sessions using a chunk-level filtering operation. Achunk-level shuffle, for example, may involve rearranging the relativeorder of the chunks, without necessarily rearranging the relative orderof observation records within a given chunk. Examples of various typesof chunk-level filtering operations are described below.

In at least some embodiments, the client may not necessarily be awarethat at least some of the filtering operations will be performed onchunks of the data set rather than at the granularity of individual datarecords. In the depicted embodiment, data transfers 1814 of the contentsof the chunks (e.g., the observation records respectively includedwithin C1, C2, C3 and C4) may be performed to load the data set into thememories of one or more MLS servers in accordance with the firstfiltering operation of the sequence. To implement the first in-memoryfiltering operation of the sequence, for example, a set of readsdirected to one or more persistent storage devices at which least someof the chunks are stored may be executed. De-compression and/ordecryption may also be required in some embodiments, e.g., prior to oneor more operations of the sequence of filtering operations 1870. Forexample, if the data is stored in compressed form at the persistentstorage devices, it may be de-compressed in accordance withde-compression instructions/metadata provided by the client ordetermined by the MLS. Similarly, if the source data is encrypted, theMLS may decrypt the data (e.g., using keys or credentials provided orindicated by the client).

After the set of reads (and/or the set of associatedde-compression/decryption operations) is completed, at least a subset ofthe chunks C1-C4 may be present in MLS server memories. (If the firstfiltering operation of the sequence involves generating a sample, forexample, not all the chunks may even have to be read in.) The remainingfiltering operations of plan 1850 may be performed in place in the MLSserver memories, e.g., without copying the contents of any of the chunksto persistent storage in the depicted embodiment, and/or withoutre-reading the content of any of the chunks from the source datalocation. For example, the in-memory results of the first filteringoperation may serve as the input data set for the second filteringoperation, the in-memory results of the second filtering operation mayserve as the input data set for the third filtering operation, and soon. In the depicted embodiment, the final output of the sequence offiltering operations may be used as input for record parsing 1818 (i.e.,determining the content of various variables of the observationrecords). The observation records 1880 generated as a result of parsingmay then be provided as input to one or more destinations, e.g., tomodel(s) 1884 and/or feature processing recipe(s) 1882. Thus, in thedepicted embodiment, only a single pass of physical read operations maybe required to implement numerous different filtering operations, whichmay result in a substantial input processing speedup compared toscenarios in which the data set is copied to persistent storage (orre-read) for each successive filtering operation. Of course, althoughmultiple chunk-level and/or observation-record-level operations may beperformed in memory without accessing persistent storage, the results ofany such operation may be stored to persistent storage if necessary,e.g., so that the results may be re-used later for another job. Thus,although avoiding frequent and potentially time-consuming I/O operationsto disk-based or other persistent storage devices is made easier by thetechnique described above, I/O to persistent storage may still beperformed at any stage as and when necessary based on an application'srequirements.

By performing filtering operations such as shuffling or sampling at thechunk level as described above, random physical read operations directedto individual data records may be avoided. Consider a scenario in whichthe input data set is to be shuffled (e.g., to cross-validate aclassification model), the shuffling is performed at the chunk levelwith a chunk size of one megabyte, the data records of the data set havean average size of one kilobyte, and neither de-compression nordecryption is required. If the original data set was 1000 megabytes insize, in any given iteration of random shuffling, the order in which1000 chunks are logically arranged may be changed. However, the order ofthe data records within any given chunk would not change in achunk-level shuffle operation. As a result, all the data records thatlie within a particular chunk (e.g., Chunk654 out of the 1000 chunks)would be provided as a group to train a model using the results of theshuffling. If the records within Chunk654 are not randomly distributedwith respect to an independent variable V1 of interest, the chunk-levelshuffle may not end up being as good with respect to randomizing thevalues of V1 for training purposes as, for example, a record-levelshuffle would have been. Thus, at least in some scenarios there may besome loss of statistical quality or predictive accuracy as a result ofperforming filtering at the chunk level rather than the data recordlevel. However, in general the loss of quality/accuracy may be keptwithin reasonable bounds by choosing chunk sizes appropriately. FIG. 19illustrates tradeoffs associated with varying the chunk size used forfiltering operation sequences on machine learning data sets, accordingto at least some embodiments.

Read operations corresponding to two example chunk mappings are shownfor a given data set DS1 in FIG. 19. To simplify the presentation, dataset DS1 is assumed to be stored on a single disk, such that a disk readhead has to be positioned at a specified offset in order to start a readoperation (either a random read or a set of sequential reads) on DS1. Inchunk mapping 1904A, a chunk size of S1 is used, and DS1 is consequentlysubdivided into four contiguous chunks starting at offsets O1, O2, O3and O4 within the data set address space. (It is noted that the numberof chunks in the example mappings shown in FIG. 19 and in subsequentfigures has been kept trivially small to illustrate the concepts beingdescribed; in practice, a data set may comprise hundreds or thousands ofchunks.) In order to read the four chunks, a total of (at least) fourread head positioning operations (RHPs) would have to be performed.After positioning a disk read head at offset O1, for example, the firstchunk comprising the contents of DS1 with offsets between O1 and O2 maybe read in sequentially. This sequential read (SR1) or set of sequentialreads may typically be fast relative to random reads, because the diskread head may not have to be repositioned during the sequential reads,and disk read head positioning (also known as “seeking”) may often takeseveral milliseconds, which may be of the same order of magnitude as thetime taken to sequentially read several megabytes of data. Thus, withthe chunk size of S1, reading the entire data set DS1 as mapped to fourchunks may involve a read operations mix 1910A that includes four slowRHPs (RHP1-RHP4) and four fast sequential reads (SR1-SR4).

Instead of using a chunk size of S, if a chunk size of 2S (twice thesize used for mapping 1904A) were used, as in mapping 1904B, only twoRHPs would be required (one to offset O1 and one to offset O3) asindicated in read operations mix 1910B, and the data set could be readin via two sequential read sequences SR1 and SR2. Thus, the number ofslow operations required to read DS1 would be reduced in inverseproportion to the chunk size used. On the X-axis of tradeoff graph 1990,chunk size increases from left to right, and on the Y-axis, the changein various metrics that results from the chunk size change isillustrated. In general, increasing the chunk size would tend todecrease the total read time (TRT) for transferring large data sets intomemory. Even if the reads of different chunks could be performed inparallel, increasing the fraction of the data that is read sequentiallywould in general tend to decrease total read time. Increasing the chunksize may in general require more memory at the MLS servers to hold thechunk contents, as indicated by the per-server memory requirement (MR)curve shown in graph 1990. Finally, as discussed above, for at leastsome types of machine learning problems, increased chunk sizes may leadto a slightly worse quality of statistics (QS) or slightly worsepredictive accuracy of machine learning models. This may occur becausethe records within a given chunk may not be filtered with respect torecords in the entire data set (or with respect to each other) in thesame way that the chunks are filtered with respect to each other. Inscenarios in which the MLS is able to select a chunk size, therefore,the tradeoffs illustrated in graph 1990 between total read time, memoryrequirements and statistical quality may have to be considered. Inpractice, depending on the size of the chunks relative to the entiredata set, the loss of statistical quality resulting from using largerchunks may be fairly small. In at least some embodiments, there need notbe a 1:1 relationship between chunks and MLS servers—e.g., a given MLSserver may be configurable to store multiple chunks of a data set. Insome embodiments, partial chunks or subsets of chunks may also be storedat an MLS server—e.g., the number of chunks stored in a given server'smemory need not be an integer. In various embodiments, in addition tochunk-level filtering operations, intra-chunk and/or cross-chunkfiltering operations (e.g., at the observation record level) may beperformed as described below in further detail, which may help tofurther reduce the loss of statistical quality. It is noted that thecurves shown in graph 1990 are intended to illustrate broad qualitativerelationships, not exact mathematical relationships. The rate at whichthe different metrics change with respect to chunk size may differ fromthat shown in the graph, and the actual relationships may notnecessarily be representable by smooth curves or lines as shown.

FIG. 20a illustrates an example sequence of chunk-level filteringoperations, including a shuffle followed by a split, according to atleast some embodiments. As shown, a chunked data set 2010 comprises tenchunks C1-C10. A detailed view of chunk C1 at the top of FIG. 20a showsits constituent observation records OR1-1 through OR1-n, with successiveobservation records being separated by delimiters 2004. As shown, theobservation records of a data set or a chunk need not be of the samesize. In a chunk-level shuffle operation 2015, which may be one of thein-memory chunk-level filtering operations of a plan 1850, the chunksare re-ordered. After the shuffle, the chunk order may beC5-C2-C7-C9-C10-C6-C8-C3-C1-C4. In a subsequent chunk-level splitoperation 2020, 70% of the chunks (e.g., C5-C2-C7-C9-C10-C6-C8) may beplaced in training set 2022, while 30% of the chunks (C3-C1-C4) may beplaced in a test set 2024 in the depicted example. As the shuffle wasperformed at the chunk level, the internal ordering of the observationrecords within a given chunk remains unchanged in the depicted example.Thus, the observation records of chunk C1 are in the same relative order(OR1-1, OR1-2, . . . , OR1-n) after the shuffle and split as they werebefore the shuffle and split filtering operations were performed. It isnoted that for at least some types of filtering operations, in additionto avoiding copies to persistent storage, the chunk contents may noteven have to be moved from one memory location to another in thedepicted embodiment. For example, instead of physically re-ordering thechunks from C1-C2-C3-C4-05-C6-C7-C8-C9-C10 toC5-C2-C7-C9-C10-C6-C8-C3-C1-C4 during the shuffle, pointers to thechunks may be modified, such that the pointer that indicates the firstchunk points to C5 instead of C1 after the shuffle, and so on.

In some embodiments, as mentioned earlier, filtering at the observationrecord level may also be supported by the MLS. For example, a client'srecord extraction request may comprise descriptors for both chunk-levelfiltering and record-level filtering. FIG. 20b illustrates an examplesequence of in-memory filtering operations that includes chunk-levelfiltering as well as intra-chunk filtering, according to at least someembodiments. In the depicted example, the same set of chunk-levelfiltering operations are performed as those illustrated in FIG. 20a—i.e., a chunk-level shuffle 2015 is performed on data set 2004,followed by a 70-30 split 2020 into training set 2022 and test set 2024.However, after the chunk-level split, an intra-chunk shuffle 2040 isalso performed, resulting in the re-arrangement of the observationrecords within some or all of the chunks. As a result of the intra-chunkshuffle, the observation records of chunk C1 may be provided as input inthe order OR1-5, OR1-n, OR1-4, OR1-1, OR1-2, . . . , to a model orfeature processing recipe (or to a subsequent filtering operation), forexample, which differs from the original order of the observationrecords prior to the chunk-level shuffle. Observation records of theother chunks (e.g., C2-C10), which are not shown in FIG. 20a or FIG. 20b, may also be shuffled in a similar manner in accordance with theclient's filtering descriptor. In at least one embodiment, cross-chunkrecord-level filtering operations may also be supported. For example,consider a scenario in which at least two chunks Cj and Ck are read intothe memory of a given MLS server S1. In a cross-chunk shuffle, at leastsome of the observation records of Cj may be shuffled or re-ordered withsome of the observation records of Ck in S1's memory. Other types ofrecord-level filtering operations (e.g., sampling, splitting, orpartitioning) may also be performed across chunks that are co-located ina given server's memory in such embodiments. In one implementation,multiple servers may cooperate with one another to perform cross-chunkoperations. For some applications, only a single chunk-level filteringoperation may be performed before the result set of the chunk-leveloperation is fed to a recipe for feature processing or to a model fortraining—that is, a sequence of multiple chunk-level operations may notbe required. Other types of operations (such as aggregation/collectionof observation records or applying aggregation functions to values ofselected variables of observation records) may also be performedsubsequent to one or more chunk-level operations in at least someembodiments.

The ability to perform filtering operations at either the chunk level orthe observation record level may enable several different alternativesto achieving the same input filtering goal. FIG. 21 illustrates examplesof alternative approaches to in-memory sampling of a data set, accordingto at least some embodiments. A 60% sample of a chunked data set 2110comprising ten chunks C1-C10 is to be obtained—that is, approximately60% of the observation records of the data set are to be retained, whileapproximately 40% of the observation records are to be excluded from theoutput of the sampling operation.

In a first approach, indicated by the arrow labeled “1”, straightforwardchunk-level sampling 2112 of the chunks may be implemented, e.g.,resulting in the selection of chunks C1, C2, C4, C6, C8 and C10 as thedesired sample. In a second approach, a combination of chunk-level andintra-chunk sampling may be used. For example, as indicated by the arrowlabeled “2”, in a first step, 80% of the chunks may be selected(resulting in the retention of chunks C1, C2, C3, C5, C6, C7, C8 and C9)using chunk-level sampling 2114. Next, in an intra-chunk sampling step2116, 75% of the observation records of each of the retained chunks maybe selected, resulting in a final output of approximately 60% of theobservation records (since 75% of 80% is 60%). In a third alternativeapproach indicated by the arrow labeled “3”, 60% of each chunk'sobservation records may be sampled in a single intra-chunk sampling step2118. Similar alternatives and combinations for achieving a given inputfiltering goal may also be supported for other types of filteringoperations in at least some embodiments.

In at least some embodiments, candidate chunk boundaries may have to beadjusted in order to ensure that individual observation records are notsplit, and to ensure consistency in the manner that observation recordsare assigned to chunks FIG. 22 illustrates examples of determining chunkboundaries based on the location of observation record boundaries,according to at least some embodiments. Data set 2202A comprisesobservation records OR1-OR7 (which may vary in size) separated by recorddelimiters such as delimiter 2265. For example, in one implementation inwhich the data source includes alphanumeric or text files, newlinecharacters (“\n”) or other special characters may be used as recorddelimiters. Based on a selected chunk size, the candidate chunkboundaries happen to fall within the bodies of the observation recordsin data set 2202A. Candidate chunk boundary (CCB) 2204A falls withinobservation record OR2 in the depicted example, CCB 2204B falls withinOR4, and CCB 2204C falls within OR6. In the depicted embodiment, thefollowing approach may be used to identify the actual chunk boundaries(ACBs). Starting at the offset immediately after the CCB for a givenchunk's ending boundary, and examining the data set in increasing offsetorder (e.g., in a sequential scan or read), the first observation recorddelimiter found is selected as the ending ACB for the chunk. Thus, inthe example of data set 2202A, the position of the delimiter between OR2and OR3 is identified as the actual chunk boundary 2214A correspondingto CCB 2204A. Similarly, ACB 2214B corresponds to the delimiter betweenOR4 and OR5, and ACB 2214C corresponds to the delimiter between OR6 andOR7. As a result of the selection of the actual chunk boundaries, asshown in chunk table 2252A, chunk C1 comprises OR1 and OR2, chunk C2comprises OR3 and OR4, and chunk C3 comprises OR5 and OR6, while chunkC4 comprises OR7. Using the technique described, each observation recordis mapped to one and only one chunk.

The same rules regarding the determination of chunk boundaries may beapplied even if a CCB happens to coincide with an OR delimiter in someembodiments. For example, in data set 2202B, CCB 2204K happens to bealigned with the delimiter separating OR2 and OR3, CCB 2204L coincideswith the delimiter separating OR4 and OR5, while CCB 2204M coincideswith the delimiter separating OR6 and OR7. Using the rule mentionedabove, in each case the search for the next delimiter starts at theoffset immediately following the CCB, and the next delimiter found isselected as the ACB. Accordingly, ACB 2214K is positioned at thedelimiter between OR3 and OR4, ACB 2214L is positioned at the delimiterbetween OR5 and OR6, and ACB 2214M is positioned at the delimiterbetween OR7 and OR8. As indicated in chunk table 2252B, chunk C1 of dataset 2202B eventually includes OR1, OR2 and OR3, chunk C2 includes OR4and OR5, chunk C3 includes OR6 and OR7, and chunk C4 includes OR8.

FIG. 23 illustrates examples of jobs that may be scheduled at a machinelearning service in response to a request for extraction of data recordsfrom any of a variety of data source types, according to at least someembodiments. As shown, a set of programming interfaces 2361 enablingclients 164 to submit observation record extraction/retrieval requests2310 in a data-source-agnostic manner may be implemented by the machinelearning service. Several different types 2310 of data sources may besupported by the MLS, such as an object storage service 2302 that maypresent a web-services interface to data objects, a block storageservice 2304 that implements volumes presenting a block-deviceinterface, any of a variety of distributed file systems 2306 (such asthe Hadoop Distributed File System or HDFS), as well as single-host filesystems 2308 (such as variants of Ext3 that may be supported byLinux-based operating systems). In at least some embodiments, databases(e.g., relational databases or non-relational databases) may also besupported data sources. Data objects (e.g., files) that are implementedusing any of the supported types of data sources may be referred to inthe retrieval requests, as indicated by the arrows labeled 2352A and2352B. In some implementations, a single client request may refer toinput data objects such as files that are located in several differenttypes of data sources, and/or in several different instances of one ormore data source types. For example, different subsets of a given inputdata set may comprise files located at two different single-host filesystems 2308, while respective subsets of another input data set may belocated at an object storage service and the block-storage service.

An MLS request handler 180 may receive a record extraction request 2310indicating a sequence of filtering operations that are to be performedon a specified data set located at one or more data sources, such assome combination of shuffling, splitting, sampling, partitioning (e.g.,for parallel computations such as map-reduce computations, or for modeltraining operations/sessions that overlap with each other in time andmay overlap with each other in the training sets used), and the like. Afiltering plan generator 2380 may generate a chunk mapping of thespecified data set, and a plurality of jobs to accomplish the requestedsequence of filtering operations (either at the chunk level, the recordlevel, or both levels) in the depicted embodiment, and insert the jobsin one or more MLS job queues 142. For example, one or more chunk readjobs 2311 may be generated to read in the data from the data source. Ifneeded, separate jobs may be created to de-compress the chunks (such asjobs 2312) and/or decrypt the data (jobs 2313). In the depictedembodiment, jobs 2314 may be generated for chunk-level filteringoperations, while jobs 2315 may be generated for observationrecord-level filtering operations. Filtering operations at theobservation record level may comprise intra-chunk operations (e.g.,shuffles of records within a given chunk) and/or cross-chunk operations(e.g., shuffles of records of two or more different chunks that may beco-located in the memory of a given MLS server) in the depictedembodiment. In at least some embodiments, respective jobs may be createdfor each type of operation for each chunk—thus, for example, if thechunk mapping results in 100 chunks, 100 jobs may be created for readingin one chunk respectively, 100 jobs may be created for the firstchunk-level filtering operation, and so on. In other embodiments, agiven job may be created for an operation involving multiple chunks,e.g., a separate job may not be required for each chunk. In someembodiments, as described below in further detail, the splitting of adata set into a training set and a test set may be implemented asseparate jobs—one for the training set and one for the test set. Asdiscussed earlier, a given job may indicate dependencies on other jobs,and such dependencies may be used to ensure that the filtering tasksrequested by the client are performed in the correct order.

FIG. 24 illustrates examples constituent elements of a record extractionrequest that may be submitted by a client using a programmatic interfaceof an I/O (input-output) library implemented by a machine learningservice, according to at least some embodiments. As shown, observationrecord (OR) extraction request 2401 may include a source data setindicator 2402 specifying the location(s) or address(es) from which theinput data set is to be retrieved. For a data set stored in an objectstorage service presenting a web-service interface, for example, one ormore URLs (uniform resource locators) or URIs (uniform resourceidentifiers) may be specified; for files, some combination of one ormore file server host names, one or more directory names, and/or one ormore file names may be provided as the indicator 2402. In oneimplementation, if a data set includes multiple objects such as morethan one file, a client may include instructions for logicalconcatenation of the objects of the data set to form a unified addressspace (e.g., the logical equivalent of “combine files of directory d1 inalphabetical order by file name, then files of directory d2 inalphabetical order”). In some embodiments, an expected format 2404 orschema for the observation records may be included in the OR extractionrequest, e.g., indicating the names of the variables or fields of theORs, the inter-variable delimiters (e.g., commas, colons, semicolons,tabs, or other characters) and the OR delimiters, the data types of thevariables, and so on. In at least one implementation, the MLS may assigndefault data types (e.g., “string” or “character”) to variables forwhich data types are not indicated by the client.

In one embodiment, the OR extraction request 2401 may includecompression metadata 2406, indicating for example the compressionalgorithm used for the data set, the sizes of the units or blocks inwhich the compressed data is stored (which may differ from the sizes ofthe chunks on which chunk-level in-memory filtering operations are to beperformed), and other information that may be necessary to correctlyde-compress the data set. Decryption metadata 2408 such as keys,credentials, and/or an indication of the encryption algorithm used onthe data set may be included in a request 2401 in some embodiments.Authorization/authentication metadata 2410 to be used to be able toobtain read access to the data set may be provided by the client inrequest 2401 in some implementations and for certain types of datasources. Such metadata may include, for example, an account name or username and a corresponding set of credentials, or an identifier andpassword for a security container (similar to the security containers390 shown in FIG. 3).

OR extraction request 2401 may include one or more filtering descriptors2412 in the depicted embodiment, indicating for example the types offiltering operations (shuffle, split, sample, etc.) that are to beperformed at the chunk level and/or at the OR level, and the order inwhich the filtering operations are to be implemented. In someimplementations, one or more descriptors 2452 may be included forchunk-level filtering operations, and one or more descriptors 2454 maybe included for record-level (e.g., intra-chunk and/or cross-chunk)filtering operations. Each such descriptor may indicate parameters forthe corresponding filtering operation—e.g., the split ratio for splitoperations, the sampling ratio for sampling operations, the number ofpartitions into which the data set is to be subdivided for parallelcomputations or parallel training sessions, the actions to be taken if arecord's schema is found invalid, and so on.

In at least one embodiment, the OR extraction request 2401 may includechunking preferences 2414 indicating, for example, a particularacceptable chunk size or a range of acceptable chunk sizes. Thedestination(s) to which the output of the filtering operation sequenceis to be directed (e.g., a feature processing recipe or a model) may beindicated in field 2416. In some embodiments, a client may indicateperformance goals 2418 for the filtering operations, such as a“complete-by” time, which may be used by the MLS to select the types ofservers to be used, or to generate a filtering sequence plan that isintended to achieve the desired goals. It is noted that in at least someembodiments, not all of the constituent elements shown in FIG. 25 may beincluded within a record extraction request—for example, the compressionand/or decryption related fields may only be included for data sets thatare stored in a compressed and/or encrypted form.

FIG. 25 is a flow diagram illustrating aspects of operations that may beperformed at a machine learning service that implements an I/O libraryfor in-memory filtering operation sequences on large input data sets,according to at least some embodiments. An I/O library that enablesclients to submit observation record extraction requests similar tothose illustrated in FIG. 24 may be implemented. The I/O library may beagnostic with respect to the type of data store at which the input dataset is stored—e.g., a common set of programmatic interfaces may beprovided for record extraction requests stored at any combination ofseveral different data store types. Such an OR extraction request may bereceived (element 2501), indicating a source data set that may be toolarge to fit into the available memory of an MLS server. The ORextraction request may include one or more descriptors indicating asequence of filtering operations that are to be performed on the inputdata set.

A chunk size to be used for transferring contiguous subsets of the inputdata set into the memories of one or more MLS servers may be determined(element 2504), e.g., based on any of various factors such as the memorycapacity constraints of the MLS servers, a preference indicated by therequesting client via parameters of the request, a default setting ofthe MLS, the estimated or actual size of the input data set, and so on.In some implementations several different chunk sizes may beselected—e.g., some MLS servers may have a higher memory capacity thanothers, so the chunks for the servers with more memory may be larger. Ifthe input data set includes multiple objects (such as files), theobjects may be logically concatenated to form a single unified addressspace (element 2507) in some embodiments. The sequence in which theobjects are concatenated may be determined, for example, based oninstructions or guidance provided in the request, based on alphanumericordering of the object names, in order of file size, in random order, orin some other order selected by the MLS.

A chunk mapping may be generated for the data set (element 2510),indicating a set of candidate chunk boundaries based on the selectedchunk size(s) and the unified address space. The positions or offsets ofthe candidate chunk boundaries within the data object or object of theinput data set may be computed as part of the mapping generationprocess. A plan for a sequence of chunk-level filtering operationscorresponding to the filtering descriptor(s) in the OR extractionrequest may be created (element 2513). The plan may include record-levelfiltering operations (e.g., intra-chunk or cross-chunk operations), inaddition to or instead of chunk-level filtering operations, in someembodiments. Cross-chunk operations may, for example, be performed onobservation records of several chunks that are co-located in the memoryof a given MLS server in some embodiments. In other embodiments,cross-chunk operations may also or instead be performed on chunks thathave been read into the memories of different MLS servers. The types offiltering operations supported may include sampling, splitting,shuffling, and/or partitioning. Based at least in part on the firstfiltering operation of the plan, a data transfer of at least a subset ofthe chunks of the data set from persistent storage to MLS servermemories may be performed (element 2516). Depending on the manner inwhich the data is stored at the source locations indicated in the ORextraction request, the data transfer process may include decryptionand/or decompression in addition to read operations in some embodiments.In some embodiments, the client may request the MLS to encrypt and/orcompress the data prior to transferring the chunks from the sourcelocations to the MLS servers, and then to perform the reverse operation(decryption and/or decompression) once the encrypted/compressed datareaches the MLS servers.

After the first filtering operation of the sequence is performed inmemory at the MLS servers, the remaining filtering operations (if any)may be performed in place in the depicted embodiment, e.g., withoutcopying the chunks to persistent storage or re-reading the chunks fortheir original source locations (element 2519). In one embodiment,respective jobs may be generated and placed in an MLS job queue for oneor more of the filtering operations. In at least some embodiments, arecord parser may be used to obtain the observation records from theoutput of the sequence of filtering operations performed (element 2522).The ORs may be provided programmatically to the requesting client (e.g.,as an array or collection returned in response to the API callrepresenting the OR extraction request), and/or to a specifieddestination such as a model or a feature processing recipe (element2525).

Consistent Filtering of Input Data Sets

FIG. 26 illustrates an example of an iterative procedure that may beused to improve the quality of predictions made by a machine learningmodel, according to at least some embodiments. The procedure may includere-splitting or re-shuffling the input data set for each of severalcross-validation iterations, for example, as described below. An inputdata set comprising labeled observation records (i.e., observationrecords for which the values or “labels” of dependent variables areknown) may be mapped to a set of contiguous chunks 2602, e.g., using thetechniques described above to increase the fraction of physical I/O thatcan be performed sequentially. An in-memory chunk-level split operation2604 may be performed to obtain a training set 2610 and a test set 2615.For example, 80% of the chunks may be included in the training set 2610in one scenario, and the remaining 20% of the chunks may be included inthe test set 2615. A candidate model 2620 may be trained in a trainingrun 2618 (e.g., for a linear regression model, candidate coefficients tobe assigned to the various independent variables of the data set may bedetermined). The candidate model 2620 may then be used to makepredictions on the test set, and the evaluation results 2625 of themodel may be obtained (e.g., indicating how accurately the model wasable to generate predictions for the dependent variables of the recordsof the test set using the candidate coefficients). A variety of measures2630 of the accuracy or quality may be obtained in differentembodiments, depending on the type of model being used—e.g., the rootmean square error (RMSE) or root mean square deviation (RMSD) may becomputed for linear regression models, the ratio of the sum of truepositives and true negatives to the size of the test set may be computedfor binary classification problems, and so on.

If the accuracy/quality measures 2630 are satisfactory, the candidatemodel 2620 may be designated as an approved model 2640 in the depictedembodiment.

Otherwise, any of several techniques may be employed in an attempt toimprove the quality or accuracy of the model's predictions. Model tuning2672 may comprise modifying the set of independent variables being usedfor the predictions, changing model execution parameters (such as aminimum bucket size or a maximum tree depth for tree-basedclassification models), and so on, and executing additional trainingruns 2618. Model tuning may be performed iteratively using the sametraining and test sets, varying some combination of independentvariables and parameters in each iteration in an attempt to enhance theaccuracy or quality of the results. In another approach to modelimprovement, changes 2674 may be made to the training and test data setsfor successive training-and-evaluation iterations. For example, theinput data set may be shuffled (e.g., at the chunk level and/or at theobservation record level), and a new pair of training/test sets may beobtained for the next round of training. In another approach, thequality of the data may be improved by, for example, identifyingobservation records whose independent variable values appear to beinvalid or outliers, and deleting such observation records from the dataset. One common approach for model improvement may involvecross-validating a candidate model using a specified number of distincttraining and test sets extracted from the same underlying data, asdescribed below with reference to FIG. 27. Just as multiple iterationsof model tuning 2672 may be performed, data set changes 2674 may also beperformed iteratively in some embodiments, e.g., until either a desiredlevel of quality/accuracy is obtained, until resources or time availablefor model improvement are exhausted, or until the changes being tried nolonger lead to much improvement in the quality or accuracy of the model.

FIG. 27 illustrates an example of data set splits that may be used forcross-validation of a machine learning model, according to at least someembodiments. In the depicted embodiment, a data set comprising labeledobservation records 2702 is split five different ways to obtainrespective training sets 2720 (e.g., 2720A-2720E) each comprising 80% ofthe data, and corresponding test sets 2710 (e.g., 2710A-2710E)comprising the remaining 20% of the data. Each of the training sets 2720may be used to train a model, and the corresponding test set 2710 maythen be used to evaluate the model. For example, in cross-validationiteration 2740A, the model may be trained using training set 2720A andthen evaluated using test set 2710A. Similarly, in cross-validationiteration 2740B, a different training set 2720B (shown in two parts,part 1 and part 2 in FIG. 27) comprising 80% of the input data may beused, and a different test set 2710B may be used for evaluating themodel. The cross-validation example illustrated in FIG. 27 may bereferred to as “5-fold cross validation” (because of the number ofdifferent training/test set pairs generated and the corresponding numberof training-and-evaluation iterations.) The MLS may implement an APIallowing a client to request k-fold cross validation in someembodiments, where k is an API parameter indicating the number ofdistinct training sets (and corresponding test sets) to be generated fortraining a specified model using the same underlying input data set.

The labeled observation records are distributed among eight chunks C1-C8in the example shown in FIG. 27. As mentioned earlier, the chunk sizesand boundaries may be determined based on any of various factors,including memory size limits at MLS servers, client preferences, and soon. In some scenarios, the split ratio desired (such as the 80-20 splitillustrated in FIG. 27) may result in the observation records of a givenchunk having to be distributed across a training set and thecorresponding test set. That is, partial chunks may have to be includedin training and test sets in some cases. Some observation records ofchunk C2 may be included in test set 2710A, while other observationrecords of chunk C2 may be included in training set 2720A, for example.

It is noted that although the training sets may appear to comprisecontiguous portions of the input data set in FIG. 27, in practice thetraining and test data sets may be obtained using random selection(e.g., either at the chunk level, at the observation record level, or atboth levels) in at least some embodiments. By changing the set ofobservation records included in the training and test sets of thedifferent cross-validation iterations 2740A-2740E, the quality of thepredictions made may in general improve, as the effect of localizednon-uniformity of the independent variable values in different subsetsof the input data set may be reduced. For example, if the value of anindependent numerical variable within the subset of data records thatare in test set 2710A is unusually high compared to the mean of thatvariable over the entire data set, the effects of that anomaly on modelaccuracy/quality would be expected to be dissipated by the use ofdifferent test data sets for the other cross-validation iterations.

FIG. 28 illustrates examples of consistent chunk-level splits of inputdata sets for cross validation that may be performed using a sequence ofpseudo-random numbers, according to at least some embodiments. A randomnumber based split algorithm 2804 is used to divide data set chunksC1-C10 into training and test sets for successive training-evaluationiterations (TEIs). Each TEI may, for example, represent a particularcross-validation iteration such as those illustrated in FIG. 27,although such training and evaluation iterations may also be performedindependently of whether cross-validation is being attempted. Apseudo-random number generator (PRNG) 2850 may be used to obtain asequence 2872 of pseudo-random numbers. The PRNG 2850 may beimplemented, for example, as a utility function or method of an MLSlibrary or a programming language library accessible from a component ofthe MLS. The state of PRNG 2850 may be deterministically initialized orreset using a seed value S (e.g., a real number or string) in thedepicted embodiment, such that the sequence of pseudo-random numbersthat is produced after resetting the state with a given seed S isrepeatable (e.g., if the PRNG is reset using the same seed multipletimes, the same sequence of PRNs would be provided after each such statereset).

In the depicted example, to simplify the presentation, the number ofchunks of the input data set (10) and the split ratio (80-20) has beenchosen such that an integer number of chunks is placed into the trainingset and the test set—i.e., observation records of a given chunk do nothave to be distributed between both a training set and a test set. Thepseudo-random numbers (PRNs) of the sequence 2872 produced by the PRNGmay be used to select members of the training and test sets. Forexample, using the first PRN 2874 (produced after resetting the state ofthe PRNG), which has a value of 84621356, chunk C7 may be selected forinclusion in the training set 2854A to be used for TEI 2890A. Using thesecond PRN 56383672, chunk C2 may be selected for the training set2854A, and so on. The random-number based split algorithm 2804 may relyon certain statistical characteristics of the PRN sequence to correctlydesignate each chunk of the input data set into either the training setor the test set in the depicted example scenario. The statisticalcharacteristics may include the property that a very large number ofdistinct pseudo-random numbers (or distinct sub-sequences of some lengthN) are expected to be produced in any given sequence (e.g., before agiven PRN is repeated in the sequence, or before a sub-sequence oflength N is repeated). If the state of the PRNG is not reset between thetime that a given training set 2854 is generated and the time that thecorresponding test set 2856 is generated in the depicted embodiment, thesequence of PRNs 2872 generated may ensure that each chunk of the inputdata is mapped to either the training set or the test set, and no chunkis mapped to both the training set and the test set. Such a splitoperation, in which each object (e.g., chunk or observation record) ofthe source data set is placed in exactly one split result set (e.g., atraining set or the corresponding test set), may be referred to as a“consistent” or “valid” split. A split operation in which one or moreobjects of the input data set are either (a) not placed in any of thesplit result sets, or (b) placed in more than one of the split resultsets may be termed an “inconsistent” or “invalid” split. The sequence ofthe PRNs used for each of the two split mappings (the mapping to thetraining set and the mapping to the test set), and hence the state ofthe PRN source, may influence the probability of producing inconsistentsplits in at least some embodiments. In turn, the use of inconsistentsplits for training and evaluation may result in poorer predictionquality and/or poorer accuracy than if consistent splits are used.

In at least some embodiments, intra-chunk shuffles may be implementedwithin the training set and/or the test set, e.g., based on contents ofa client request in response to which the TEIs are being implemented.Thus, for example, the observation records within a given chunk (e.g.,C7) of training set 2854A may be re-ordered in memory (without copyingthe records to persistent storage) relative to one another before theyare provided as input to the model being trained. Similarly, theobservation records of a given chunk (e.g., C3) of test set 2856A may beshuffled in memory before the model is evaluated using the test set.

As a result of using the PRN sequence 2872, the first TEI 2890A may beimplemented with a training set 2854A of chunks(C7,C2,C4,C5,C9,C1,C10,C8) and a test set 2856A of chunks (C3,C6). Insome embodiments, the same PRNG 2850 may also be used (e.g., withoutre-initialization or resetting), to split the input data set for thenext TEI 2890B. It is noted that for some models and/or applications,only one TEI may be implemented in various embodiments. In the depictedexample, training set 2854B of TEI 2890B comprises chunks(C8,C3,C5,C6,C10,C2,C1,C9) and the corresponding test set 2856Bcomprises chunks (C4,C7). Both the splits illustrated in FIG. 28 areconsistent/valid according to the definitions provided above. It isnoted that although the splitting of the data is illustrated at thechunk level in FIG. 28, the same type of relationship between the PRNGstate and the consistency of the split may apply to splits at theobservation record level (or splits involving partial chunks) in atleast some embodiments. That is, to perform a consistent split at theobservation record level using a PRNG, the state of the PRNG shouldideally not be re-initialized between the determination of the trainingset and the determination of the test set. A split involving partialchunks may be implemented in some embodiments as a chunk-level split inwhich a non-integer number of chunks is placed in each split result set,followed by an intra-chunk split for those chunks whose records aredistributed across multiple split result sets. In addition to two-waysplits, the PRN-based approach to splitting a data set may also be usedfor N-way splits (where N>2).

FIG. 29 illustrates an example of an inconsistent chunk-level split ofan input data set that may occur as a result of inappropriatelyresetting a pseudo-random number generator, according to at least someembodiments. In the depicted example, a PRNG 1850 is initialized using aseed S. The PRN sequence 2972A is used by the split algorithm 2804 toproduce the training set 2954A comprising the same set of chunks of dataset 2844A that were included in test set 2854A of FIG. 28(C7,C2,C4,C5,C9,C1,C10,C8). After the training set 2954A is generated,the PRNG is re-initialized. As a result, the sequence of pseudo-randomnumbers generated is repeated—e.g., the first PRN generated after thereset is once again 84621356, the second PRN is once again 56383672, andso on. The split algorithm chooses chunks C7 and C2 for inclusion intest set 2956A as a result of the repetition of PRNs in the depictedexample. Such a split may be deemed invalid or inconsistent because C2and C7 are in both the training set and the test set (and because chunksC3 and C6 are in neither the training set nor the test set).

In some embodiments, a PRNG may not be invoked in real time for eachplacement of a given chunk or record into a training set or a test set.Instead, a list of pseudo-random numbers or random numbers may begenerated beforehand (e.g., using a PRNG), and the numbers in thepre-generated list may be used one by one for the split placements. Insuch a scenario, as long as a pointer is maintained to the last numberin the list that was used for the training set, and the test setplacement decisions are made using the remainder of the numbers (i.e.,numbers that were not used for the training set), split consistency maybe achieved in at least some embodiments.

In another approach to attaining consistent splits, respectivemechanisms (e.g., APIs) may be implemented to (a) save a current stateof a PRNG and (b) to re-set a PRNG to a saved state in one embodiment.Consider a scenario in which an API “save_state(PRNG)” can be invoked tosave the internal state of a PRNG to an object “state_AfterTraining”after the training set of a TEI has been generated, and a different API“set_state(PRNG, state_AfterTraining)” can be invoked to reset the stateof the PRNG (or a different PRNG) to the saved state just beforestarting the selection of the test set of the TEI. Using such a pair ofstate save and restore operations, the same sequence of PRNs may beobtained as would be obtained if all the PRNs were obtained withoutsaving/re-setting the PRNG state. In some embodiments, different PRNsources may be used for the training set selection than of a given TEIare used for the test set selection, as described below with respect toFIG. 30, and the state of such PRN sources may be synchronized to helpachieve consistent splits.

In at least some embodiments, the selection of a test set from a giveninput data set may occur asynchronously with respect to (and in somecases much later than) the selection of the corresponding training set.For example, separate jobs may be inserted in the MLS job queue for theselection of a training set and the selection of the corresponding testset, and the jobs may be scheduled independently of each other in amanner similar to that described earlier. In such scenarios, in order toensure that the training/test split is valid and consistent despite thedelay between the two operations, the MLS may maintain state informationpertaining to the selection of the training set in some embodiments,which can then be used to help generate the test set. FIG. 30illustrates an example timeline of scheduling related pairs of trainingand evaluation jobs, according to at least some embodiments. Four eventsthat occur during a period of approximately four hours (from 11:00 to15:00 on a particular day) of a job scheduler's timeline are shown.

At time t1, a training job J1 of a training-and-evaluation iterationTEI1 for a model M1 is begun. Job J1 is scheduled at a set of serversSS1 of the MLS, and may include the selection of a training set, e.g.,either at the chunk-level, at the observation record level, or at bothlevels. A pseudo-random number source PRNS 3002 (such as a function ormethod that returns a sequence of PRNs, or a list of pre-generated PRNs)may be used to generate the training set for Job J1. At time t2, atraining job J2 may be scheduled at a server set SS2, for atraining-and-evaluation iteration TEI2 for a different model M2. Thetraining set for job J2 may be obtained using pseudo-random numbersobtained from a different PRNS 3002B.

At time t3, a test job J3 for the evaluation phase of TEI1 is scheduled,more than two hours later than job J1. The scheduling of J3 may bedelayed until J1 completes, for example, and the size of the data setbeing used for J1/J3 may be so large that it takes more than two hoursto complete the training phase in the depicted example. J3 may bescheduled at a different set of servers SS3 than were used for J1. In atleast some implementations, a different PRNS 9002C may be available atserver set SS3 than was available at server set SS1. In order to ensureconsistency of the training/test split, PRNS 3002C may be synchronizedwith PRNS 3002A in the depicted embodiment. Thus for example, if a seedvalue Seed1 was used to initialize PRNS 3002A, and 1000 pseudo-randomnumbers were obtained from PRNS 3002A during job J1, the same seed valueSeed1 may be used to initialize a logically equivalent PRNS 3002C, and1000 pseudo-random numbers may be acquired from PRNS 3002C before thepseudo-random numbers to be used for test set selection are acquired.Equivalents of the “save_state( )” and “set_state( )” calls discussedabove may be used in some embodiments to synchronize PRNS 3002C withPRNS 3002A. If lists of pre-generated PRNS are being used as the PRNsources, in one embodiment the MLS may ensure that (a) the same list isused for J1 and J3 and (b) the first PRN in the list that is used for J3is in a position immediately after the position of the last PRN used forJ1. Other synchronization techniques may be used in various embodimentsto ensure that the sequence of pseudo-random numbers used for test setdetermination is such that a valid and consistent split is achieved forjobs J1 and J3. Similarly, for test job J4 (scheduled at t4)corresponding to training job J2, PRNS 3002D may be synchronized withPRNS 3002B. In at least the depicted embodiment, to ensure splitconsistency, it may be necessary to enforce a logical relationship orsome degree of coordination between the sets of pseudo-random numbersused for generating a training set and the corresponding test set (e.g.,the numbers used in J3 may have to be coordinated with respect to thenumbers used in J1, and the numbers used in J4 may have to becoordinated with respect to the numbers used in J2).

FIG. 31 illustrates an example of a system in which consistency metadatais generated at a machine learning service in response to a clientrequest, according to at least some embodiments. The consistencymetadata may be retained or shared across related jobs (e.g., a trainingjob and a corresponding evaluation job) to achieve the kinds ofcoordination/synchronization discussed with respect to FIG. 30. Insystem 3100 of FIG. 31, a client 164 of an MLS may submit a splitrequest 3110 via a data-source-agnostic programmatic interface 3161 ofan MLS I/O library. In some implementations, the split request may bepart of a cross-validation request, or part of a request to perform aspecified number of training-and-evaluation iterations. In at least oneembodiment, the split request may represent a variant of the type ofobservation record extraction request 2401 shown in FIG. 24. The splitrequest may include, for example, one or more client-specified seedvalues 3120 that may be used for obtaining the pseudo-random numbers forthe requested split operations, although such seed values may not haveto be provided by the client in at least one embodiment. In addition, inthe depicted embodiment, the split request 3110 may include anindication (e.g., file names, paths or identifiers) of the input dataset 3122. Split parameters 3124 may indicate one or moretraining-to-test ratios (e.g., the 80-20 split ratio illustrated in FIG.29). In some embodiments in which the split request is part of a requestfor training-and-evaluation iterations or cross-validation iterations,the desired iteration count 3126 may be included in the client request.

A request handler component 180 of the MLS may pass on the request 3110to a plan generator 3180 in the depicted embodiment. The plan generatormay determine a set of consistency metadata 3152, e.g., metadata thatmay be shared among related jobs that are inserted in the MLS job queuefor the requested split iterations. The metadata 3152 may comprise theclient-provided seed values 3120, for example. In one embodiment, if aclient-provided seed value is not available (e.g., because the API 3161used for the client request does not require a seed to be provided, orbecause the client failed to provide a valid seed value), the plangenerator 3180 may determine a set of one or more seed values. SuchMLS-selected seed values may be based, for example, on some combinationof input data set IDs 3122 (e.g., a hash value corresponding to a filename or directory name of the input data set may be used as a seed),client identifier, the time at which the request 3110 was received, theIP address from which the request 3110 was received, and so on. In oneimplementation, the MLS may have several sources of pseudo-randomnumbers available, such as PRNGs or lists of pre-generated PRNs, and anidentifier of one or more PRN sources may be included in the consistencymetadata 3152. In an embodiment in which pre-generated PRN lists are tobe used, a pointer to the last-used PRN within a specified list may beused, such that each entity that uses the list (e.g., an MLS jobexecutor) updates the pointer after it has used some number of thelist's PRNs. In one embodiment in which equivalents of the “save_state()” and “set_state( )” operations described above are supported forPRNGs, a state record of a PRNG may be included in the metadata. Thestate record may be updated by each entity (e.g., an MLS job executor)that used the PRNG, e.g., so that the next entity that uses the PRNG canset its state appropriately to obtain PRNs that can be used to perform aconsistent split.

The plan generator 3180 may generate respective jobs 3155 for selectingthe split result sets. For example, for a given training-and-evaluationiteration, one job may be created for selecting the training set andanother job may be generated for selecting the test set. In someimplementations, a job object created by the plan generator 3180 mayinclude a reference or pointer to the consistency metadata to be usedfor that job. In another implementation, at least a portion of theconsistency metadata 3152 may be included within a job object. When ajob is executed, the metadata 3152 may be used to ensure that the inputdata set is split consistently. In some embodiments, a single job may becreated that includes both training and test set selection.

It is noted that a similar approach towards consistency or repeatabilitymay be taken for other types of input filtering operations, such assampling or shuffling, in at least some embodiments. For example, in oneembodiment, a client may wish to ensure shuffle repeatability (i.e.,that the results of one shuffle request can be re-obtained if a secondshuffle request with the same input data and same request parameters ismade later) or sample repeatability (i.e., that the same observationrecords or chunks are retrievable from a data set as a result ofrepeated sample requests). If the filtering operation involves a use ofpseudo-random numbers, saving seed values and/or the other types ofconsistency metadata shown in FIG. 31 may enable support for shufflerepeatability and/or sample repeatability as well. For example, arepeated shuffle may be obtained starting with the same input data setand re-initializing a PRNG with the same seed value as was used for aninitial shuffle. Similarly, re-using the same seed may also result in arepeatable sample. In various embodiments, consistent splits may beperformed at the chunk level, at the observation record level, or atsome combination of chunk and record levels, using consistency metadataof the kind described above. In at least one embodiment, after achunk-level split is performed, the records of the individual chunks inthe training set or the test set may be shuffled prior to use fortraining/evaluating a model.

FIG. 32 is a flow diagram illustrating aspects of operations that may beperformed at a machine learning service in response to a request fortraining and evaluation iterations of a machine learning model,according to at least some embodiments. As shown in element 3201, arequest to perform one or more TEIs (training-and-evaluation iterations,such as cross-validation iterations) may be received via a programmaticinterface such as an MLS I/O library API. A set of consistency metadatamay be generated for the iteration(s), e.g., comprising one or moreinitialization parameter values (such as a value V1) for pseudo-randomnumber sources (PRNSs). The metadata may comprise a seed value to beused to initialize or reset a state of a PRNG, for example, or a pointerto a particular offset within a list of pre-generated pseudo-randomnumber. In some embodiments, the client may include at least a portionof the metadata in the TEI request. In addition to or instead of seedvalue(s), the consistency metadata may include, for example, anidentifier of a PRNS, a representation of a state of a PRNS, and/or apointer into a list of pseudo-random numbers.

If the input data set indicated in the request is spread over multiplefiles or multiple data objects, the files/objects may be logicallyconcatenated to form a unified address space for the input data. Theaddress space of the input data set may be sub-divided into contiguouschunks (element 3207), e.g., with the chunk sizes/boundaries beingselected based on client preferences, memory constraints at MLS servers,and/or other factors. One or more chunks of the input data set may beread in from persistent storage to respective memories at one or moreMLS servers, e.g., such that at least a portion of chunk C1 is stored inmemory at server S1 and at least a portion of chunk C2 is stored inmemory at server S2 (element 3210).

Using the consistency metadata, a first training set Trn1 of the inputdata may be selected (element 3213), e.g., including at least someobservation records of chunk C1. The training set may be selected at thechunk level, the observation record level, or some combination of chunklevel and observation record level. Partial chunks may be included inthe training set Trn1 in at least some embodiments (that is, someobservation records of a given chunk may be included in the training setwhile others may eventually be included in the corresponding test set).In one embodiment, an initialization parameter value V1 may be used toobtain a first set of pseud-random numbers from a source that provideddeterministic sequences of such numbers based on the source's initialstate, and the first set of pseudo-random numbers may in turn be used toselect the training set Trn1 used to train a targeted machine learningmodel M1.

To evaluate the model after it has been trained, a test set Tst1 may bedetermined using the consistency metadata (element 3216) (e.g., using aset of pseudo-random numbers obtained from the same source, or from asource whose state has been synchronized with that of the source usedfor selecting Trn1). In one implementation, for example, the consistencymetadata may indicate a seed Seed1 and a count N1 of pseudo-randomnumbers that are obtained from a PRNG for generating Trn1. If theoriginal PRNG is not available to provide pseudo-random numbers forselecting Tst1 (e.g., if the test set is being identified at a differentserver than the server used for identifying Trn1, and local PRNGs haveto be used at each server), an equivalent PRNG may be initialized withSeed1, and the first N1 pseudo-random numbers generated from theequivalent PRNG may be discarded before using the succeedingpseudo-random numbers (starting from the (N1+1)th number) for selectingTst1. In another implementation, the algorithm used for selecting Trn1and Tst1 (or any pair of training and test sets) may be designed in sucha way that the same sequence of pseudo-random numbers can be used toselect Trn1 and Tst1 while still meeting the consistency criteriadescribed earlier. In such an implementation, same seed value may beused to initialize a PRNG for Tst1, and no pseudo-random numbers mayhave to be skipped to select Tst1. Model M1 may be tested/evaluated(e.g., the accuracy/quality of the model's predictions may bedetermined) using test set Tst1.

As long as more TEIs remain to be performed (as determined in element3219), the training and test sets for the next iteration may beidentified in place, without copying any of the chunk contents to otherlocations in the depicted embodiment (element 3222). In the depictedembodiment, the consistency metadata that was used to generate Trn1 andTst1 may be used for selecting the training set and the test set forsubsequent TEIs as well. In other embodiments, respective sets ofconsistency metadata may be used for respective TEIs. In at least someembodiments in which a training set is initially identified at the chunklevel, the observation records within individual chunks of the trainingset may be shuffled in memory (i.e., an intra-chunk shuffle may beperformed without any additional I/O to persistent storage) prior tousing the observation records to train the model. Similarly, intra-chunkshuffles may be performed on test sets in some embodiments before thetest sets are used for evaluation. After all the requested iterations oftraining and evaluation are completed, the processing of the requestreceived in operations corresponding to element 3201 may be consideredcomplete, and the final results of the iterations may be provided to adestination indicated in the request (element 3225).

Category-Based Sampling of Imbalanced Data Sets

Many machine learning problems are directed towards predictingoccurrences of rare or unusual events. For example, a fraud detectionmachine learning algorithm may be used to predict fraudulenttransactions, which may sometimes occur at very low rates in practice,such as less than once per thousand transactions. Similarly, a tumoridentification image processing algorithm may have to detectabnormalities that only occur once in thousands (or even hundreds ofthousands) of patients. The data sets that are used to make predictionsor classifications in such scenarios are said to comprise at least twocategories or classes of observations: the majority category or class(representing the more frequent scenario with respect to the dependentvariable to be predicted, such as non-fraudulent transactions or healthypatients), and one or more minority categories or classes (representingthe less frequent scenario that is often of greater interest than themore frequent scenario with respect to the problem being solved, such asfraudulent transactions or patients with tumors). Often, the number ofobservation records in the majority category is so much higher than thenumber of observation records in the minority category (or categories)that using an unmodified data set, or a randomly sampled data set, totrain a machine learning model may result in poor prediction results. Amodel trained using a very small fraction of minority category recordsmay not be able to capture the factors that can be used for accuratepredictions of the minority category dependent variable values. Datasets in which one category of observation records vastly outnumbersothers may be termed “imbalanced” herein.

FIG. 33 illustrates an example of an imbalanced training set that may beused at a machine learning service, according to at least someembodiments. Observation records 4002 of a raw or unmodified imbalanceddata set are classified into a majority category 4012 and two minoritycategories, 4014A and 4014B on the basis of the values of a dependentvariable DV. It is noted that for some applications, observation recordsmay include more than one dependent variable. To simplify thepresentation, a scenario with a single dependent variable DV isconsidered in the following discussion, although the category-basedsampling techniques described may be used for applications with multipledependent variables as well. The dependent variable DV can take on anyof three values in the example shown in FIG. 33: Vx, Vy and Vz.Observation records with a DV value equal to Vx are in majority category4012, those with a DV value of Vy are in minority category 4014A, whilethe remaining records with DV values of Vz are in minority category4014B. The majority category 4012 may comprise hundreds of thousands (oreven millions) of observation records, while the population of theminority categories may be a few thousand in the depicted embodiment.

If the entire imbalanced data set is used to train a model, or even ifrandom samples of the observation records are used for training 4020, anumber of different problems may be encountered. Because the number ofminority observation is so small relative to the majority observationrecords, the number of observation records with DV values Vy or Vz thatare provided as input to the model may be quite low. As a result, theaccuracy 4050 of predictions of minority values of DV (Vy and Vz) may below. In a model in which respective coefficients are determined for somenumber of independent variables, for example, the coefficients resultingfrom training using the imbalanced input data may be biased towardspredicting Vx as the value of the independent variable. In at least somescenarios, independent variables that may be helpful in predicting Vy orVz may not even be included in the set of variables used for makingpredictions for the model if an imbalanced training set is used.Furthermore, depending on how many observation records are included inthe training set, long training times 4052 may be required, since eachobservation record included in the training set may have to be read andanalyzed. In addition, in some embodiments, the model's runtime memoryfootprint 4054 may be larger if a large imbalanced training set is used.For example, in decision-tree based models, it may be the case thatdecision trees with more nodes are created with a larger imbalancedtraining set than would have been created with a smaller and morebalanced training set were used, and the memory footprint of the modelat prediction time may depend at least to some extent on the number ofnodes as discussed above.

In at least some embodiments, an automated approach to sampling thetraining set categories, in which for example different sampling ratiosor different values of other sampling parameters are selected by the MLSfor various categories, may be used to reduce some or all of theproblems indicated above for imbalanced training data. Such an approach,in which one or more sampling parameters differ for different categoriesof observation records, may be referred to herein as category-basedsampling, category-dependent sampling, class-based sampling, orclass-dependent sampling. FIG. 34 illustrates an example ofcategory-based sampling of a training set, according to at least someembodiments. In the depicted embodiment, a client of the MLS may submita request (e.g., using an MLS API or other programmatic interface) totrain or generate a model, indicating that the training set 4002 is tobe used and specifying the dependent variable DV to be predicted. Inresponse to receiving an indication of the training set 4002 (e.g., at afront-end request handler component of the MLS), a component of the MLS(such as a model manager or model trainer) may analyze at least a subsetof the observation records of the training set to identify the majorityand minority values of the dependent variable DV. In some embodimentsthe MLS may include a separate preliminary data analyzer component thatis responsible for identifying majority and minority categories, e.g.,either by reading the entire training set or a sample of the trainingset. In some implementations, the programmatic interface used for thetraining request may enable the client to specify the majority valueand/or the minority values. In at least some embodiments, the client maynot specify sampling ratios to be used for any of the observation recordcategories, or for the training set as a whole; in fact, the client maynot indicate any specific sampling-related requirements.

The model manager may determine whether one or more imbalance criteriaare met by the training set 4002, e.g., by comparing the number ofmajority category observation records found in its analysis with thenumber of minority category observation records. For example, in onescenario, for a training set to be considered imbalanced enough totrigger category-based sampling, a ratio of 100:1 between the majoritycategory population and the smallest minority category population may berequired. In some cases the imbalance criteria may be determined by theMLS by using its knowledge base of best practices—e.g., for certainproblem domains, a majority-to-minority ratio of 1000:1 may triggercategory-based sampling, while for other problem domains best resultsmay be obtained if category-based sampling is used even with amajority-to-minority ratio as small as 50:1. In embodiments in which thedependent variables take on several different minority values, ratiosbetween the populations of the different minority categories may also beincluded in the imbalance criteria. Other ratios and/or other types ofimbalance criteria may be used in various embodiments.

In the example depicted in FIG. 34, the MLS determines that theimbalance criteria are met by training set 4002. The MLS may determinewhether samples are to be obtained for some or all of the categoriesidentified in the training set, and if so, the sampling ratios to beused (e.g., what fraction of the observation records of the categoriesof the original training set are to be retained) in the depictedembodiment. A number of performance goals and/or other factors may betaken into account when determining whether sampling is to be used for agiven category of observation records and/or the specific samplingparameters to be used—e.g., the total amount of memory or storage spacetaken up by the category relative to the memory available at an MLSserver to be used for training, the number of minority categories found,a training time goal for the model, accuracy goals for the model'spredictions, execution time goals for predictions, and so on. In thedepicted example, the MLS determines that a sampled or modified trainingset 4102 should comprise a 1% sample 4112 of the majority category 4012,a 33% sample of minority category 4014A, and a 50% sample of minoritycategory 4014B. The percentages of the observation records that areretained (e.g., 1%, 33% and 50% in the illustrated example) may also bereferred to as sampling ratios herein.

Having identified the categories that are to be sampled based on one ormore sampling criteria, the MLS may select and implement a respectivesampling methodology for each category. For example, random samplingwith replacement, random sampling without replacement, or any of variousiterative sampling approaches may be used to perform automated samplingoperations 4115 in various embodiments. In some embodiments, differentsampling methodologies may be used for different categories for sometypes of machine learning problems. After a sampled training set 4102 isobtained, the model may be trained, as indicated by arrow 4120. In someembodiments, a respective sampled training set 4102 may be generated foreach of several training iterations. As a result of the category-basedsampling approach, at least in some cases the trained model'spredictions may have higher prediction accuracy 4150 (at least for theminority categories with DV values Vy and Vz) than if the un-sampled andimbalanced training set 4002 had been used. In addition, the reductionin the size of the training set may lead to shorter training times 4152and/or smaller model memory footprints 4154 in at least someembodiments. An indication of the model that has been trained using thesampled training set 4102 may be provided to the client that requestedthe training in the depicted embodiment.

The client on whose behalf the category-based sampling is performed inan automated fashion in the depicted embodiment may not even have tospecify that sampling is to be used, and may not even be aware that animbalance exists in the unmodified training set. Instead of the clienthaving to provide parameters for sampling or conduct samplingexperiments, the MLS may automatically determine whether sampling shouldbe used, and if so, the sampling parameters and methodology to be usedfor each category to be sampled. As a result, even relativelyinexperienced clients may be able to benefit from the improvements inprediction accuracy, training time and run-time performance that mayaccrue from category-based sampling in the depicted embodiment.

It is noted that not all the categories may be sampled in somescenarios—for example, the population of a minority category may be sosmall that all the observation records of that category may be retainedin the sampled training set. It is also noted that in some embodiments,automated sampling of observation records may be performed even if thetraining set does not meet one or more imbalance criteria. For example,in one embodiment, if the unmodified training set is larger than athreshold size, a 10% sample of all the categories may be obtained evenif the ratio of the majority category population to the minoritycategory population is 60:40. In some scenarios, several differentminority categories may be sampled, e.g., at different sampling ratios.

FIG. 35 illustrates examples of factors that may influence a selectionof sampling parameters to be used for a training set, according to atleast some embodiments. A number of different types of samplingparameters 4252 may have to be identified in different embodiments, suchas the sampling ratio for each category, the sampling methodology to beused, whether samples should be generated in parallel or sequentially,and so on. It is noted that in some cases, as mentioned above, the MLSmay decide not to sample a given category of observation records, andmay include the entire population of such a category in thepost-sampling training set. In the latter scenario, the sampling ratioselected may be considered to be 100%; that is, the decision not tosample a category may be considered the logical equivalent of sampling100% of the category. If the size 4212 of the unmodified training setexceeds a threshold, the MLS may consider the training set a candidatefor category-based sampling in the depicted embodiment. Themajority-to-minority ratios 4214 (i.e., the ratios of the counts ofobservation records that fall into each of the identified categoriesbased on dependent variable values) may also influence the samplingparameters. Resource constraints 4216 of the MLS servers that are to beused for training the model may also play a role in the selection ofsampling ratios and other parameters. Such resource constraints mayinclude, for example, memory constraints (e.g., the amount of mainmemory configured at an MLS server), CPU or processing constraints,storage constraints such as available disk space, network constraints,and so on.

In at least one embodiment, a client may indicate training timeconstraints 4218 (or training budget constraints that can be translatedinto training time constraints), which may also influence samplingparameter selection. For example, the client may indicate that trainingis to be completed in no more than X hours, or is to consume no morethan $Y of a client budget. Based on per-hour billing rates, the budgetgoal may be converted by the MLS into a resource goal or a time goal,and the sampling parameters may be selected accordingly. Similarly, insome embodiments, clients may indicate time or budget constraints 4220for model execution (i.e., for prediction runs of the model), and suchgoals may also influence the sampling parameters.

In some embodiments, a set of prediction accuracy goals 4222 may bedetermined for the model, and the sampling parameters may be selectedbased at least in part on such goals. For example, a client may indicatethat a false positive rate for a binary classification model is not toexceed X %, and the sampling ratios selected for the majority andminority categories by the MLS may be based at least in part on such agoal. In at least one embodiment, the MLS may have its own model qualityor accuracy goals (e.g., internally-determined goals that may be used inscenarios in which clients do not have to specify such goals or fail tospecify such goals) that may influence sampling parameters.

For some machine learning techniques or algorithms, a minimum populationmay be required for each category of observation records. Such minimumpopulation constraints 4224 may also be used to select samplingparameters 4252 in various embodiments—e.g., if the minimum count ofrecords for a given category is 1000 and the un-sampled training setonly contains 1010 observation records of a given category, a samplingratio of 100% may be chosen for that category.

As mentioned earlier, in some embodiments a MLS may maintain a knowledgebase 122 (shown in FIG. 1) of best practices for various machinelearning problem domains. Over time, entries collected in such aknowledge base may indicate respective sampling parameters that workwell with different domains—for example, a minimum ratio R1 between theretained majority and minority category populations may result in goodpredictions for fraud detection, while a different minimum ratio R2 maybe better for medical diagnosis applications. If a client indicates theparticular problem domain for which a model is to be trained, or if theMLS is able to deduce the problem domain from various attributes such asthe algorithms to be used or the names of files or variables of thetraining data, some sampling parameters may be selected based on thedomain-specific best practices 4210 in the depicted embodiment. Otherfactors not shown in FIG. 35 may influence the selection of samplingparameters in different embodiments. In some embodiments, at least someof the factors illustrated in FIG. 35 may also or instead be used forfirst determining whether category-based sampling is to be performed atall, and then for determining the specific sampling parameters to beused for various categories.

FIG. 36 illustrates an example sequence of interactions between a clientand a machine learning service configured to automate category-basedsampling of data sets, according to at least some embodiments. In thedepicted system 4300, a client 164 of a machine learning service maysubmit a model creation or model training request 4310 using aprogrammatic interface 4361 implemented by the MLS. The request 4310 mayinclude an indicator (e.g., a file name or a data source identifier) ofa training data set 4322 and one or more goals or constraints 4324, suchas training time or model execution time goals or budgets, modelaccuracy/quality goals, and the like.

An MLS front end request/response handler 4380 may receive the request4310 and transmit it to a back-end model manager 4382 in the depictedembodiment. The model manager 4382 may generate several different jobspertaining to the creation/training of the model. For example, one ormore reading and/or categorization jobs 4355 may be generated andinserted into a job queue in some embodiments. Such jobs 4355 may, forexample, perform chunk-level I/O similar to that described earlier toread at least a subset of the training data set, and the observationrecords may be subdivided into a majority category and one or moreminority categories based at least in part on the values of one or moredependent variables. Decisions regarding whether any of the categoriesare to be sampled, e.g., based on imbalances detected in the categorypopulations and/or the client's goals/constraints may be made by themodel manager 4382.

If a decision to sample one or more of the categories is reached, one ormore sampling jobs 4358 may be generated to select the subsets of thedifferent categories in accordance with sampling ratios and/or othersampling parameters selected by the model manager 4382. Training jobs4361 may be generated and scheduled to train a model or models using thesampled training sets in the depicted embodiment. In at least someembodiments, respective evaluation jobs 4364 may also be generated andscheduled at the MLS, e.g., to compare the prediction accuracy orquality of several different models 4366 before selecting one model asthe recommended model. The models may be stored in the MLS repository120. In embodiments in which several different models are evaluated, themost accurate model (or, in cases where no single model is significantlymore accurate than the others, one of a group of models whoseperformance or accuracy is at least as good as that of the other models)may be selected as the default model 4372, and an identifier or pointerto the model may be provided to the client 164. The client 164 maydecide to modify one or more model parameters in some embodiments, andmay optionally submit model parameter change requests 4378 to the MLS.Such changes may then be applied to the models stored in the MLSartifact repository 120. Category-based sampling may be used for avariety of model types in different embodiments, including for examplevarious types of linear and/or non-linear models, regression models,decision-tree based models such as Random Forest models, and the like.

In some embodiments, sampling for supervised learning techniques such asregression or classification may be used in conjunction withunsupervised learning techniques such as clustering. FIGS. 37a and 37billustrate respective sequences of operations in which clustering may beused together with category-based sampling to train models at a machinelearning service, according to at least some embodiments. As shown inFIG. 37a , the observation records of an unlabeled data set 4402 may beclassified into categories 4421A, 4421B and 4421C using a clusteringalgorithm 4404. A clustering model may be executed using one or morejobs of the MLS in some embodiments. Labels or dependent variable valuesindicating the categories identified using the clustering algorithm maybe applied or added to each observation record to obtain categorizeddata set 4420A. Category-dependent sampling 4430 (e.g., with respectivesampling ratios for different categories) may then be performed toobtain samples of each category to be used for one or more trainingsessions 4434. Thus, the labels that are used to identify majority andminority categories for which category-dependent sampling is used may begenerated by an unsupervised learning model in at least someembodiments.

In the approach illustrated in FIG. 37a , the clustering algorithm ormodel may be applied to the entire unlabeled data set 4402. In scenariosin which the unlabeled data set is very large, a global sampling stepmay be implemented before clustering is performed, so that the size ofthe input data for the clustering algorithm is reduced. Such an approachis illustrated in FIG. 37b . Global sampling 4403 may be applied tounlabeled data set 4402 (e.g., using chunk-level sampling and/orobservation record-level sampling as described earlier), and the resultof the global sampling (e.g., a 10% sampling of the unlabeled data set)may be provided as input to the clustering model or algorithm 4404. Theclustering algorithm may identify categories such as 4421A-4421C, onwhich category-dependent sampling 4430 may be applied to obtain thesampled training data to be used for training 4434. In some embodimentsand for certain types of applications or problem domains, severaldifferent global samples may be obtained and classified usingclustering, with the results of the clustering being used forcategory-based sampling. Other types of unsupervised training approachesmay be used together with category-dependent sampling in variousembodiments.

FIG. 38 is a flow diagram illustrating aspects of operations that may beperformed at a machine learning service that provides automatedcategory-based sampling of imbalanced data sets, according to at leastsome embodiments. As shown in element 4501, an indication of a trainingdata set to be used to create or train one or more models may bereceived, e.g., via a programmatic interface such as an API at afront-end request handler component of an MLS. In some embodiments, thetraining set may be specified either in a client's request to create anew model, or in a request to train (or re-train) an existing model. Inone embodiment, when a request to generate a new model is received, theMLS may experiment with several different models (or several differentmodel parameter combinations) before identifying a particular model tobe provided to the client as the best or default model. In at least someembodiments the client may not indicate any requirements, preferences orparameters related to sampling in the request. In such embodiments, theMLS may be responsible for determining whether any type of sampling(whether category-based or category-independent) should be used. In atleast one embodiment, the client may indicate an unlabeled data set, andthe MLS may use clustering or a similar approach to first generate thelabels and then determine whether to use category-dependent sampling.

As shown in element 4504, the MLS (e.g., a component such as apreliminary data analyzer, model manager, or model trainer of the MLS)may examine the indicated training set to identify a majority categoryand one or more minority categories of the observation records of thetraining data set. In some embodiments, a separate job may be generatedand scheduled for the identification of the majority and minoritycategories. The MLS may determine whether one or more criteria forimplementing automated sampling are met by the training set (element4507). The order in which criteria that may lead to a decision toperform automated sampling are checked may differ in differentimplementations. In one implementation, for example, an imbalancecriterion may be checked first, e.g., to determine whether the ratio ofthe populations of the different categories of observation records issuch that category-based sampling is advisable. In anotherimplementation, one or more performance criteria (such as training timegoals, model execution goals and the like) may be considered first inview of the population or size of the training set (or thepopulation/size of the individual categories) to decide whetherautomated sampling should be considered as an option, and then theimbalance criteria may be taken into account to decide whether thesampling should be category-based or category-independent. In someembodiments, category-based sampling may be performed if the imbalancebetween category populations is high enough, regardless of other factorssuch as the absolute populations of the categories orperformance-related goals. For example, category-based sampling may beperformed if the ratio between the majority and the smallest minoritycategory is greater than 1000:1 in one implementation, even if theentire training set is small enough to fit into a single MLS server'smemory. In such embodiments, a particular criterion (which may bedependent on the problem domain being addressed) may be given higherpriority relative to other criteria when deciding whether automatedsampling should be used.

If a decision to perform automated sampling is reached, samplingparameters for the particular categories to be sampled may be identified(element 4510), e.g., by the model manager. The parameters may include,for example, the sampling ratios to be used, the sampling methodology tobe used (e.g., random sampling with replacement, random sampling withoutreplacement, stratified sampling, iterative sampling based on predictionerror values), whether samples are to be generated in parallel orsequentially for the different categories, and so on. In someembodiments, a sampling ratio of 100% may be selected for somecategories, e.g., for a category whose absolute population is verylow—that is, the entire category may be retained instead of discardingany of the records of the category. In at least one embodiment, adecision to sample only the majority category may be made—that is,automated sampling of minority categories may not necessarily beperformed. Any combination of various factors such as those illustratedin FIG. 35 may be taken into consideration when determining the samplingparameters in different embodiments. Thus, the kinds of factors shown inFIG. 35 may be used for the initial decision to perform category-basedsampling, for the selection of specific sampling parameters for thedifferent categories to be sampled, or for both the initial decision andthe selection of specific sampling parameters in various embodiments.After the parameters have been selected, the samples may be generated orobtained (element 4513). In some embodiments, respective jobs may becreated and scheduled to extract each of the samples; in otherembodiments, a single job may be used to obtain the samples for themajority as well as the minority categories. One or more models may betrained and evaluated using the sampled training data (element 4516).

If a decision not to perform automated sampling is reached in operationscorresponding to element 4507, training may be performed using theentire un-sampled training data set (element 4519). If multiple modelsare trained, the best-performing model (e.g., the model with the mostaccurate predictions among those trained) may be selected as the defaultmodel to be provided to the client (element 4522); otherwise, if asingle model is trained (e.g., based on budget constraints or trainingtime constraints), that single model may be provided as the defaultmodel to the client. The client may be allowed to change variousparameters of the model in at least some embodiments as indicated inFIG. 36.

In some embodiments, as mentioned above, straightforward random sampling(with or without replacement) may be used for category-based sampling.In other embodiments, sampling may be performed iteratively based onresults of training the model using previous iterations. FIG. 39 is aflow diagram illustrating aspects of operations that may be performed ata machine learning service to perform sampling iterations usingerror-based sampling weights, according to at least some embodiments.The iterative weight-dependent sampling technique illustrated in FIG. 39may be used either for the entire training set, or at the level ofindividual categories (with different parameters being used fordifferent categories), in various embodiments. In the depictedembodiment, sampling weights may be attached to at least a subset of theobservation records to be sampled. The sampling weight of a given recordmay be indicative of a probability of that record being selected in(i.e., retained in) the sample—so, for example, the probability ofretaining a record with a weight of 0.5 may be five times greater thanthe probability of retaining a record with a weight of 0.1.

As shown in element 4601, an initial set of equal sampling weights{W-orig} may be selected for all the observation records for whichiterative sampling is to be used (e.g., either the records of the entiretraining set Tr1, or the records of an individual category C1). For afirst training iteration, a model M1 may be trained using an X % sample(e.g., 1% sample) of the observation records selected using theirassigned weights (element 4604). The trained model may be evaluatedusing the un-sampled training data (element 4607) in the depictedembodiment. The absolute values of the prediction errors may be obtainedfor all the observation records. The error values may then be normalized(e.g., mapped to a real number between 0 and 1), and converted into anew set of sampling weights {W-next} to be used for the next iterationof sampling and training, such that the probability of choosing a givenrecord for the next iteration may be proportional to the normalizederror computed for the given record in the current iteration. Thus, anobservation record for which the prediction error was high may be morelikely to be selected for the next iteration than an observation recordfor which the prediction error was low. This approach may be based onthe assumption that training using observation records for which largeprediction errors occurred may enhance the prediction accuracy ofsubsequent iterations to a greater extent that training usingobservation records for which the model has already been able to makegood predictions.

The training set may then be sampled for the next iteration (element4610) using the newly-assigned weights, to obtain Sample-n. For example,an X % sample may be generated again, either with or without replacementin different implementations. In some embodiments, the observationrecords selected in the previous iteration may be retained—that is, theprevious iteration's sampling result may be added to the sampling resultof the current iteration, while in other embodiments only the currentiteration's records may be retained. The model M1 may be trained withthe new training sample (Sample-n) (element 4613). An upper bound on thenumber of iterations performed may be imposed in the depictedembodiment, e.g., to ensure that the iterative sampling techniqueeventually terminates and does not keep consuming resourcesindefinitely. If the maximum limit on the number of iterations performedhas been reached (as detected in element 4616), no more iterations maybe performed (element 4619) and the training may be concluded. If themaximum limit on iterations has not been reached, and if the errormetrics (e.g., mean absolute error computed for the training set)resulting from training using the new sample has improved relative tothe previous iteration (as detected in element 4618), a new sampling andtraining iteration may be implemented. For example, the error valuesfrom the just-completed iteration may be normalized and used as weightsfor the next iteration, and operations corresponding to element 4607onwards may be repeated. If the improvement in the error metrics isbelow a threshold, or if there is no improvement in the error metrics(as also detected in element 4618), no more sampling iterations may beneeded (element 4619), and the training of the model may also beconcluded. As indicated above, different parameters (e.g., differentsampling ratios) may be used for each category in a weight-basediterative sampling approach; that is, the use of error values to selectobservation records may be applied in a category-dependent manner in atleast some embodiments.

In at least one embodiment, a number of different components of themachine learning service may collectively perform the operationsassociated with category-based sampling. A client request for thetraining or creation of a model, submitted via one or more APIs may bereceived at a request/response handler, which may determine the natureof the request and pass on the client request (or an internalrepresentation of the client request) to a model trainer or modelmanager. In some embodiments, the initial categorization of labeledtraining data (e.g., to identify majority and minority categories) maybe performed by a preliminary data analyzer. In embodiments in which thetraining data is unlabeled, a clustering manager component may beresponsible for generating labels that can later be used forcategory-based sampling. In some embodiments, the decisions regardingsampling parameters may be made by an optimizer component of the MLS oran optimizer subcomponent of the model manager. Sampling managers may beinstantiated for each of the sampling methodologies in some embodiments.In at least one embodiment, one or more training servers of the MLS maybe used for training models, while one or more prediction servers may beused for the actual predictions. In embodiments in which respective jobsare created for different tasks, a job manager may be responsible formaintaining a collection or queue of outstanding jobs and for schedulingjobs as resources become available and job dependencies are met.Responses (e.g., an identifier of a default model, which the client maythen modify if needed) may be provided to the client by the front-endrequest/response handler in some embodiments. In at least someembodiments, some or all of these components may comprise specialized,tuned, or task-optimized hardware and/or software.

It is noted that in various embodiments, operations other than thoseillustrated in the flow diagrams of FIGS. 9a, 9b, 10a, 10b , 17, 25, 32,38 and 39 may be used to implement at least some of the techniques of amachine learning service described above. Some of the operations shownmay not be implemented in some embodiments, may be implemented in adifferent order, or in parallel rather than sequentially. For example,with respect to FIG. 9b , a check as to whether the client's resourcequota has been exhausted may be performed subsequent to determining theworkload strategy in some embodiments, instead of being performed beforethe strategy is determined.

Use Cases

The techniques described above, of providing a network-accessible,scalable machine learning service that is geared towards users with awide range of expertise levels in machine learning tools andmethodologies may be beneficial for a wide variety of applications.Almost every business organization or government entity is capable ofcollecting data on various aspects its operations today, and thediscovery of meaningful statistical and/or causal relationships betweendifferent components of the collected data and the organization'sobjectives may be facilitated by such a service. Users of the MLS maynot have to concern themselves with the details of provisioning thespecific resources needed for various tasks of machine learningworkflows, such as data cleansing, input filtering, transformations ofcleansed data into a format that can be fed into models, or modelexecution. Best practices developed over years of experience withdifferent data cleansing approaches, transformation types, parametersettings for transformations as well as models may be incorporated intothe programmatic interfaces (such as easy-to learn and easy-to-use APIs)of the MLS, e.g., in the form of default settings that users need noteven specify. Users of the MLS may submit requests for various machinelearning tasks or operations, some of which may depend on the completionof other tasks, without having to manually manage the scheduling ormonitor the progress of the tasks (some of which may take hours or days,depending on the nature of the task or the size of the data setinvolved).

A logically centralized repository of machine learning objectscorresponding to numerous types of entities (such as models, datasources, or recipes) may enable multiple users or collaborators to shareand re-use feature-processing recipes on a variety of data sets. Expertusers or model developers may add to the core functionality of the MLSby registering third-party or custom libraries and functions. The MLSmay support isolated execution of certain types of operations for whichenhanced security is required. The MLS may be used for, and mayincorporate techniques optimized for, a variety of problem domainscovering both supervised and unsupervised learning, such as, frauddetection, financial asset price predictions, insurance analysis,weather prediction, geophysical analysis, image/video processing, audioprocessing, natural language processing, medicine and bioinformatics andso on. Specific optimization techniques such as pruning of depth-firstdecision trees and/or category-specific sampling may be implemented bydefault in some cases without the MLS clients even being aware of theuse of the techniques.

Illustrative Computer System

In at least some embodiments, a server that implements one or more ofthe components of a machine learning service (including control-planecomponents such as API request handlers, input record handlers, recipevalidators and recipe run-time managers, plan generators, jobschedulers, artifact repositories, and the like, as well as data planecomponents such as MLS servers implementing decision tree optimizationsand/or category-based sampling) may include a general-purpose computersystem that includes or is configured to access one or morecomputer-accessible media. FIG. 40 illustrates such a general-purposecomputing device 9000. In the illustrated embodiment, computing device9000 includes one or more processors 9010 coupled to a system memory9020 (which may comprise both non-volatile and volatile memory modules)via an input/output (I/O) interface 9030. Computing device 9000 furtherincludes a network interface 9040 coupled to I/O interface 9030.

In various embodiments, computing device 9000 may be a uniprocessorsystem including one processor 9010, or a multiprocessor systemincluding several processors 9010 (e.g., two, four, eight, or anothersuitable number). Processors 9010 may be any suitable processors capableof executing instructions. For example, in various embodiments,processors 9010 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitableISA. In multiprocessor systems, each of processors 9010 may commonly,but not necessarily, implement the same ISA. In some implementations,graphics processing units (GPUs) may be used instead of, or in additionto, conventional processors.

System memory 9020 may be configured to store instructions and dataaccessible by processor(s) 9010. In at least some embodiments, thesystem memory 9020 may comprise both volatile and non-volatile portions;in other embodiments, only volatile memory may be used. In variousembodiments, the volatile portion of system memory 9020 may beimplemented using any suitable memory technology, such as static randomaccess memory (SRAM), synchronous dynamic RAM or any other type ofmemory. For the non-volatile portion of system memory (which maycomprise one or more NVDIMMs, for example), in some embodimentsflash-based memory devices, including NAND-flash devices, may be used.In at least some embodiments, the non-volatile portion of the systemmemory may include a power source, such as a supercapacitor or otherpower storage device (e.g., a battery). In various embodiments,memristor based resistive random access memory (ReRAM),three-dimensional NAND technologies, Ferroelectric RAM, magnetoresistiveRAM (MRAM), or any of various types of phase change memory (PCM) may beused at least for the non-volatile portion of system memory. In theillustrated embodiment, program instructions and data implementing oneor more desired functions, such as those methods, techniques, and datadescribed above, are shown stored within system memory 9020 as code 9025and data 9026.

In one embodiment, I/O interface 9030 may be configured to coordinateI/O traffic between processor 9010, system memory 9020, and anyperipheral devices in the device, including network interface 9040 orother peripheral interfaces such as various types of persistent and/orvolatile storage devices. In some embodiments, I/O interface 9030 mayperform any necessary protocol, timing or other data transformations toconvert data signals from one component (e.g., system memory 9020) intoa format suitable for use by another component (e.g., processor 9010).In some embodiments, I/O interface 9030 may include support for devicesattached through various types of peripheral buses, such as a variant ofthe Peripheral Component Interconnect (PCI) bus standard or theUniversal Serial Bus (USB) standard, for example. In some embodiments,the function of I/O interface 9030 may be split into two or moreseparate components, such as a north bridge and a south bridge, forexample. Also, in some embodiments some or all of the functionality ofI/O interface 9030, such as an interface to system memory 9020, may beincorporated directly into processor 9010.

Network interface 9040 may be configured to allow data to be exchangedbetween computing device 9000 and other devices 9060 attached to anetwork or networks 9050, such as other computer systems or devices asillustrated in FIG. 1 through FIG. 39, for example. In variousembodiments, network interface 9040 may support communication via anysuitable wired or wireless general data networks, such as types ofEthernet network, for example. Additionally, network interface 9040 maysupport communication via telecommunications/telephony networks such asanalog voice networks or digital fiber communications networks, viastorage area networks such as Fibre Channel SANs, or via any othersuitable type of network and/or protocol.

In some embodiments, system memory 9020 may be one embodiment of acomputer-accessible medium configured to store program instructions anddata as described above for FIG. 1 through FIG. 39 for implementingembodiments of the corresponding methods and apparatus. However, inother embodiments, program instructions and/or data may be received,sent or stored upon different types of computer-accessible media.Generally speaking, a computer-accessible medium may includenon-transitory storage media or memory media such as magnetic or opticalmedia, e.g., disk or DVD/CD coupled to computing device 9000 via I/Ointerface 9030. A non-transitory computer-accessible storage medium mayalso include any volatile or non-volatile media such as RAM (e.g. SDRAM,DDR SDRAM, RDRAM, SRAM, etc.), ROM, etc., that may be included in someembodiments of computing device 9000 as system memory 9020 or anothertype of memory. Further, a computer-accessible medium may includetransmission media or signals such as electrical, electromagnetic, ordigital signals, conveyed via a communication medium such as a networkand/or a wireless link, such as may be implemented via network interface9040. Portions or all of multiple computing devices such as thatillustrated in FIG. 47 may be used to implement the describedfunctionality in various embodiments; for example, software componentsrunning on a variety of different devices and servers may collaborate toprovide the functionality. In some embodiments, portions of thedescribed functionality may be implemented using storage devices,network devices, or special-purpose computer systems, in addition to orinstead of being implemented using general-purpose computer systems. Theterm “computing device”, as used herein, refers to at least all thesetypes of devices, and is not limited to these types of devices.

Embodiments of the disclosure can be described in view of the followingclauses:

-   -   1. A system, comprising:    -   one or more computing devices configured to:        -   receive, via a particular programmatic interface of a set of            programmatic interfaces implemented at a network-accessible            machine learning service of a provider network, a first            request from a client to perform a particular operation            associated with an instance of an entity type, wherein the            entity type comprises one or more of: (a) a data source to            be used for a machine learning model, (b) a set of            statistics to be computed from a particular data source, (c)            a set of feature processing transformation operations to be            performed on a specified data set, (d) a machine learning            model employing a selected algorithm, (e) an alias            associated with a machine learning model, or (f) a result of            a particular machine learning model;        -   insert a job object corresponding to the first request in a            job queue of the machine learning service;        -   provide an indication to the client that the first request            has been accepted for execution;        -   determine, in accordance with a first workload distribution            strategy identified for the first request, a first set of            provider network resources to be used to perform the            particular operation;        -   receive, prior to a completion of the particular operation            indicated in the first request, a second request from the            client to perform a second operation dependent on a result            of the particular operation;        -   insert a second job object corresponding to the second            request in the job queue, wherein the second job object            includes an indication of a dependency of the second            operation on a result of the particular operation;        -   prior to initiating execution of the second operation,            provide a second indication to the client that the second            request has been accepted for execution; and        -   in response to a determination that the particular operation            has been completed successfully, schedule the second            operation on a second set of provider network resources.    -   2. The system as recited in clause 1, wherein the particular        operation comprises one or more of: (a) a creation of the        instance, (b) a read operation to obtain respective values of        one or more attributes of the instance, (c) a modification of an        attribute of the instance, (d) a deletion of the instance, (e) a        search operation, or (f) an execute operation.    -   3. The system as recited in any of clauses 1-2, wherein the        particular operation comprises assignment of an alias usable by        a designated group of users of the machine learning service to        execute a particular machine learning model, wherein the alias        comprises a pointer to the particular machine learning model,        wherein at least some users of the designated group of users are        not permitted to modify the pointer.    -   4. The system as recited in any of clauses 1-3, wherein the set        of programmatic interfaces comprises a representational state        transfer application programming interface.    -   5. The system as recited in any of clauses 1-4, wherein the        particular operation comprises a creation of a particular data        source, wherein the one or more computing devices are further        configured to:        -   generate a particular set of statistics on one or more            variables of data records of the particular data source,            without receiving a request from the client for the            particular set of statistics; and        -   provide, to the client, an indication of the particular set            of statistics.    -   6. A method, comprising:    -   performing, by one or more computing devices:        -   receiving, via a particular programmatic interface of a set            of programmatic interfaces implemented at a machine learning            service, a first request from a client to perform a            particular operation associated with an instance of an            entity type, wherein the entity type comprises one or more            of: (a) a data source to be used for generating a machine            learning model, (b) a set of feature processing            transformation operations to be performed on a specified            data set, (c) a machine learning model employing a selected            algorithm, or (d) an alias associated with a machine            learning model;        -   inserting a job corresponding to the first request in a job            queue of the machine learning service;        -   receiving, prior to a completion of the particular operation            indicated in the first request, a second request from the            client to perform a second operation dependent on a result            of the particular operation;        -   inserting a second job object corresponding to the second            request in the job queue, wherein the second job object            includes an indication of a dependency of the second            operation on a result of the particular operation; and        -   in response to determining that the particular operation has            been completed successfully, scheduling the second            operation.    -   7. The method as recited in clause 6, wherein the particular        operation comprises one or more of: (a) a creation of the        instance, (b) a read operation to obtain respective values of        one or more attributes of the instance, (c) a modification of an        attribute of the instance, (d) a deletion of the instance, (e) a        search operation, or (f) an execute operation.    -   8. The method as recited in any of clauses 6-7, wherein the        particular operation comprises assignment of an alias usable by        a designated group of users of the machine learning service to        execute a particular machine learning model, wherein the alias        comprises a pointer to the particular machine learning model,        wherein at least some users of the designated group of users are        not permitted to modify the pointer.    -   9. The method as recited in any of clauses 6-8, wherein the        particular operation comprises a creation of a particular data        source, further comprising performing, by the one or more        computing devices:    -   generating a particular set of statistics on one or more        variables of data records of the particular data source, without        receiving a request from the client for the particular set of        statistics; and    -   providing, to the client, an indication of the particular set of        statistics.    -   10. The method as recited in clause 9, further comprising        performing, by the one or more computing devices:    -   selecting a subset of the data records of the particular data        source to be used to generate the particular set of statistics.    -   11. The method as recited in any of clauses 6-9, further        comprising performing, by the one or more computing devices:    -   identifying a workload distribution strategy for the first        request, wherein said identifying comprises one or more of: (a)        determining a number of passes of processing a data set of the        particular operation (b) determining a parallelization level for        processing a data set of the particular operation, (c)        determining a convergence criterion to be used to terminate the        particular operation, (d) determining a target durability level        for intermediate data produced during the particular operation,        or (e) determining a resource capacity limit for implementing        the particular operation.    -   12. The method as recited in clause 11, further comprising        performing, by the one or more computing devices:    -   selecting a particular set of provider network resources to        implement the first workload strategy.    -   13. The method as recited in any of clauses 6-9 or 11, further        comprising performing, by the one or more computing devices:    -   in response to determining that performing the particular        operation includes an execution of a module developed by an        entity external to the provider network, identifying a        particular security container from which to select at least one        resource to be used for the particular operation.    -   14. The method as recited in any of clauses 6-9, 11 or 13,        further comprising performing, by the one or more computing        devices:    -   providing, to the client, an executable version of a particular        machine learning model for execution at a platform outside the        provider network.    -   15. The method as recited in any of clauses 6-9, 11, or 13-14,        further comprising performing, by the one or more computing        devices:    -   verifying, prior to scheduling the particular operation, that a        resource quota of the client has not been exhausted.    -   16. A non-transitory computer-accessible storage medium storing        program instructions that when executed on one or more        processors:    -   receive, via a particular programmatic interface of a set of        programmatic interfaces implemented at a network-accessible        machine learning service of a provider network, a first request        from a client to perform a particular operation associated with        an instance of an entity type, wherein the entity type comprises        one or more of: (a) a data source to be used for generating a        machine learning model, (b) a set of statistics to be computed        from a particular data source, (c) a machine learning model        employing a selected algorithm, or (d) an alias associated with        a machine learning model;    -   insert a job corresponding to the first request in a job queue        of the machine learning service;    -   receive, prior to a completion of the particular operation        indicated in the first request, a second request from the client        to perform a second operation dependent on a result of the        particular operation; and insert a second job object        corresponding to the second request in the job queue, wherein        the second job object includes an indication of a dependency of        the second operation on a result of the particular operation.    -   17. The non-transitory computer-accessible storage medium as        recited in clause 16, wherein the particular operation comprises        assignment of an alias usable by a designated group of users of        the machine learning service to execute a particular machine        learning model, wherein the alias comprises a pointer to the        particular machine learning model, wherein at least some users        of the designated group of users are not permitted to modify the        pointer.    -   18. The non-transitory computer-accessible storage medium as        recited in any of clauses 16-17, wherein the particular        operation comprises a creation of a particular data source,        wherein the instructions when executed at the one or more        processors:    -   generate a particular set of statistics on one or more variables        of data records of the particular data source, without receiving        a request from the client for the particular set of statistics;        and    -   provide, to the client, an indication of the particular set of        statistics.    -   19. The non-transitory computer-accessible storage medium as        recited in clause 18, wherein one or more variables comprise a        plurality of variables, and wherein the instructions when        executed on the one or more processors:    -   identify, based at least in part on a correlation analysis of        the plurality of variables, a first set of candidate variables        to be used in preference to a second set of variables as inputs        to a machine learning model; and    -   provide an indication of the first set of variables to the        client.    -   20. The non-transitory computer-accessible storage medium as        recited in any of clauses 16-18, wherein the particular        operation comprises an instantiated of a particular machine        learning model in online mode, wherein the instructions when        executed on the one or more processors:    -   select a set of provider network resources to be used for the        particular machine learning model in online mode based at least        in part on an expected workload level indicated by the client.    -   21. The non-transitory computer-accessible storage medium as        recited in any of clauses 16-18 or 20, wherein the instructions        when executed on the one or more processors:    -   receive, from the client of the service, credentials to be used        to decrypt one or more data records of a particular data source        to perform the particular operation.    -   22. The non-transitory computer-accessible storage medium as        recited in any of clauses 16-18 or 20-21, wherein the        instructions when executed on the one or more processors:    -   receive a third request from the client via an idempotent        programmatic interface of the set of programmatic interfaces to        perform a third operation;    -   determine, based on one or more of: (a) an instance identifier        indicated in the third request, (b) an identifier of the client,        or (c) a representation of input parameters of the third        request, whether the third request is a duplicate of an        earlier-submitted request; and    -   in response to a determination that the third request is a        duplicate of an earlier-submitted request, provide an indication        of success of the third request to the client, without inserting        an additional job object corresponding to the third request in        the job queue.

Embodiments of the disclosure can also be described in view of thefollowing clauses:

-   -   1. A system, comprising:    -   one or more computing devices configured to:        -   receive, at a network-accessible machine learning service of            a provider network, a text representation of a recipe            comprising one or more of: (a) a group definitions section            indicating one or more groups of variables, wherein            individual ones of the one or more groups comprise a            plurality of variables on which at least one common            transformation operation is to be applied, (b) an assignment            section defining one or more intermediate variables, (c) a            dependency section indicating respective references to one            or more machine learning artifacts stored in a repository,            or (d) an output section indicating one or more            transformation operations to be applied to at least one            entity indicated in the group definitions section, the            assignment section, or the dependency section;        -   validate, in accordance with (a) a set of syntax rules            defined by the machine learning service and (b) a set of            library function definitions for transformation operation            types supported by the machine learning service, the text            representation of the recipe;        -   generate an executable representation of the recipe;        -   store the executable representation in the repository;        -   determine that the recipe is to be applied to a particular            data set;        -   verify that the particular data set meets a run-time            acceptance criterion of the recipe; and        -   apply, using one or more selected provider network            resources, a particular transformation operation of the one            or more transformation operations to the particular data            set.    -   2. The system as recited in clause 1, wherein the one or more        computing devices are further configured to:    -   receive a request to apply the recipe to a different data set;    -   verify that the different data set meets the run-time acceptance        criterion of the recipe; and    -   apply the particular transformation operation to the different        data set.    -   3. The system as recited in any of clauses 1-2, wherein the one        or more computing devices are further configured to:    -   provide, to a client via a programmatic interface, an indication        of a respective set of one or more recipes applicable to        individual ones of a plurality of machine learning problem        domains.    -   4. The system as recited in any of clauses 1-3, wherein the text        representation comprises an indication of a particular machine        learning model to be executed using a result of the particular        transformation operation.    -   5. The system as recited in any of clauses 1-4, wherein the one        or more computing devices are further configured to:    -   determine, in response to an indication that automated parameter        tuning is to be performed for the recipe, a plurality of        parameter value options applicable to a different transformation        operation of the one or more transformation operations;    -   generate, by the machine learning service, respective results of        the different transformation operation using individual ones of        the plurality of parameter value options; and    -   provide, by the machine learning service based on an analysis of        the respective results, an indication of at least one candidate        parameter value of the plurality of parameter value options that        meets a parameter acceptance criterion.    -   6. A method, comprising:    -   performing, by one or more computing devices:        -   receiving, at a network-accessible machine learning service,            a first representation of a recipe comprising one or more            of: (a) a group definitions section indicating one or more            groups of variables, wherein individual ones of the one or            more groups comprise a plurality of data set variables on            which at least one common transformation operation is to be            applied and (b) an output section indicating one or more            transformation operations to be applied to at least one            entity indicated in one or more of: (i) the group            definitions section or (ii) an input data set;        -   validating, in accordance with at least a set of library            function definitions for transformation operation types            supported by the machine learning service, the first            representation of the recipe;        -   generating an executable representation of the recipe;        -   determining that the recipe is to be applied to a particular            data set;        -   verifying that the particular data set meets a run-time            acceptance criterion; and        -   applying, using one or more selected provider network            resources, a particular transformation operation of the one            or more transformation operations to the particular data            set.    -   7. The method as recited in clause 6, wherein the first        representation is a text representation or a binary        representation.    -   8. The method as recited in any of clauses 6-7, wherein the        first representation is generated by a client of the machine        learning service using a tool obtained from the machine learning        service.    -   9. The method as recited in any of clauses 6-8, wherein a data        type of at least one variable of an input data record of the        particular data set comprises one or more of: (a) text, (b) a        numeric data type, (c) Boolean, (d) a binary data type, (d) a        categorical data type, (e) an image processing data type, (f) an        audio processing data type, (g) a bioinformatics data type,        or (h) a structured data type.    -   10. The method as recited in clause 9, wherein the data type        comprises a particular structured data type, further comprising        performing, by the one or more computing devices:    -   selecting, based at least in part on the particular structured        data type, a particular library function to be used for the        particular transformation operation.    -   11. The method as recited in any of clauses 6-9, wherein the        first representation comprises an assignment section defining an        intermediate variable in terms of one or more of: (a) an input        data set variable or (b) an entity defined in the group        definitions section, wherein the intermediate variable is        referenced in the output section.    -   12. The method as recited in any of clauses 6-9 or 11, wherein        the first representation comprises a dependency section        indicating a reference to a particular artifact stored in a        repository of the machine learning service, wherein the        particular transformation operation consumes an output of the        particular artifact as an input.    -   13. The method as recited in clause 12, wherein the particular        artifact comprises one or more of: (a) a machine learning        model, (b) a different recipe, (c) a statistics set or (d) an        alias that includes a reference to a machine learning model.    -   14. The method as recited in any of clauses 6-9 or 11-12,        wherein the particular transformation operation utilizes a        user-defined function, further comprising performing, by the one        or more computing devices:    -   receiving, at the machine learning service from a client prior        to said receiving the first representation, an indication of a        module implementing the user-defined function, wherein the        module is in a text format or a binary format.    -   15. The method as recited in any of clauses 6-9, 11-12 or 14,        further comprising performing, by the one or more computing        devices:    -   validating the first representation in accordance with a set of        syntax rules defined by the machine learning service.    -   16. The method as recited in any of clauses 6-9, 11-12, or        14-15, further comprising performing, by the one or more        computing devices:    -   receiving a request to apply the recipe to a different data set;    -   verifying that the different data set meets the run-time        acceptance criterion of the recipe; and    -   applying the particular transformation operation to the        different data set.    -   17. The method as recited in any of clauses 6-9, 11-12, or        14-16, further comprising performing, by the one or more        computing devices:    -   providing, to a client via a programmatic interface, an        indication of a respective set of one or more recipes applicable        to individual ones of a plurality of machine learning problem        domains.    -   18. The method as recited in any of clauses 6-9, 11-12, or        14-17, wherein the first representation comprises an indication        of a particular machine learning model to be executed using a        result of the particular transformation operation.    -   19. The method as recited in any of clauses 6-9, 11-12, or        14-18, further comprising performing, by the one or more        computing devices:    -   determining, by the machine learning service in response to an        indication that automated parameter tuning is to be performed        for the recipe, a plurality of parameter value options        applicable to a different transformation operation of the one or        more transformation operations;    -   generating, by the machine learning service, respective results        of the different transformation operation using individual ones        of the plurality of parameter value options.    -   20. The method as recited in clause 19, further comprising        performing, by the one or more computing devices:    -   selecting, by the machine learning service, a particular        parameter value of the plurality of parameter value options as        an acceptable value based at least in part on a particular        result set corresponding to the particular parameter value.    -   21. The method as recited in any of clauses 19-20, further        comprising performing, by the one or more computing devices:    -   indicating, by the machine learning service to a client, at        least a subset of the plurality of parameter value options as        candidate values based on an analysis of the respective results;        and    -   receiving, at the machine learning service from the client, an        indication of a particular parameter value of the subset to be        used for the different transformation operation.    -   22. The method as recited in any of clauses 19-21, wherein the        plurality of parameter value options comprise one or more        of: (a) respective lengths of n-grams to be derived from a        language processing data set, (b) respective quantile bin        boundaries for a particular variable, (c) image processing        parameter values, (d) a number of clusters into which a data set        is to be classified, (e) values for a cluster boundary        threshold, or (f) dimensionality values for a vector        representation of a text document.    -   23. A non-transitory computer-accessible storage medium storing        program instructions that when executed on one or more        processors:    -   determine, at a machine learning service, a first representation        of a recipe comprising one or more of: (a) a group definitions        section indicating one or more groups of variables, wherein        individual ones of the one or more groups comprise a plurality        of data set variables on which at least one common        transformation operation is to be applied, or (b) an output        section indicating one or more transformation operations to be        applied to at least one entity indicated in one or more of (i)        the group definitions section or (ii) an input data set of the        recipe;    -   validate, in accordance with at least a set of library function        definitions for transformation operation types supported by the        machine learning service, the first representation of the        recipe;    -   generate an executable representation of the recipe; and    -   in response to a determination that the recipe is to be applied        to a particular data set, use one or more selected provider        network resources to implement a particular transformation        operation of the one or more transformation operations to the        particular data set.    -   24. The non-transitory computer-accessible storage medium as        recited in clause 23, wherein the first representation comprises        an assignment section defining an intermediate variable in terms        of one or more of: (a) an input data set variable or (b) an        entity defined in the group definitions section, wherein the        intermediate variable is referenced in the output section.    -   25. The non-transitory computer-accessible storage medium as        recited in any of clauses 23-24, wherein the first        representation comprises a dependency section indicating a        reference to a particular artifact stored in a repository of the        machine learning service, wherein the particular transformation        operation consumes an output of the particular artifact as an        input.    -   26. The non-transitory computer-accessible storage medium as        recited in any of clauses 23-25, wherein the particular artifact        comprises one or more of: (a) a machine learning model, (b) a        different recipe, (c) an alias or (d) a set of statistics.    -   27. The non-transitory computer-accessible storage medium as        recited in any of clauses 23-26, wherein the set of library        function definitions comprise one or more of: (a) a quantile bin        function, (b) a Cartesian product function, (c) a bi-gram        function, (d) an n-gram function, (e) an orthogonal sparse        bigram function, (f) a calendar function, (g) an image        processing function, (h) an audio processing function, (i) a        bio-informatics processing function, or (j) a natural language        processing function.

Embodiments of the disclosure can also be described in view of thefollowing clauses:

-   -   1. A system, comprising:    -   one or more computing devices configured to:        -   receive, via a programmatic interface of a machine learning            service of a provider network, a request to extract            observation records of a particular data set from one or            more file sources, wherein a size of the particular data set            exceeds a size of a first memory portion available for the            particular data set at a first server of the machine            learning service;        -   map the particular data set to a plurality of contiguous            chunks, including a particular contiguous chunk whose size            does not exceed the first memory portion;        -   generate, based at least in part on a filtering descriptor            indicated in the request, a filtering plan to perform a            sequence of chunk-level filtering operations on the            plurality of contiguous chunks, wherein an operation type of            individual ones of the sequence of filtering operations            comprises one or more of: (a) sampling, (b) shuffling, (c)            splitting, or (d) partitioning for parallel computation, and            wherein the filtering plan includes a first chunk-level            filtering operation followed by a second chunk-level            filtering operation;        -   execute, to implement the first chunk-level filtering            operation, at least a set of reads directed to one or more            persistent storage devices at which at least a subset of the            plurality of contiguous chunks are stored, wherein,            subsequent to the set of reads, the first memory portion            comprises at least the particular contiguous chunk;        -   implement the second chunk-level filtering operation on an            in-memory result set of the first chunk-level filtering            operation, without re-reading from the one or more            persistent storage devices, and without copying the            particular contiguous chunk; and        -   extract a plurality of observation records from an output of            the sequence of chunk-level filtering operations.    -   2. The system as recited in clause 1, wherein the one or more        computing devices are further configured to:    -   implement an intra-chunk filtering operation on a set of        observation records identified within the particular contiguous        chunk.    -   3. The system as recited in any of clauses 1-2, wherein the one        or more computing devices are further configured to:    -   de-compress contents of the particular contiguous chunk in        accordance with one or more de-compression parameters indicated        in the request.    -   4. The system as recited in any of clauses 1-3, wherein the one        or more computing devices are further configured to:    -   decrypt contents of the particular contiguous chunk in        accordance with one or more decryption parameters indicated in        the request.    -   5. The system as recited in any of clauses 1-4, wherein the one        or more computing devices are further configured to:    -   provide a plurality of observation records obtained from the        sequence as input for an execution of one or more of: (a) a        feature processing recipe or (b) a machine learning model.    -   6. A method, comprising:    -   performing, on one or more computing devices:        -   receiving, at a machine learning service, a request to            extract observation records of a particular data set from            one or more data sources;        -   mapping the particular data set to a plurality of chunks            including a particular chunk;        -   generating a filtering plan to perform a sequence of            chunk-level filtering operations on the plurality of chunks,            wherein an operation type of individual ones of the sequence            of filtering operations comprises one or more of: (a)            sampling, (b) shuffling, (c) splitting, or (d) partitioning            for parallel computation, and wherein the filtering plan            includes a first chunk-level filtering operation followed by            a second chunk-level filtering operation;        -   initiating, to implement the first chunk-level filtering            operation, a set of data transfers directed to one or more            persistent storage devices at which at least a subset of the            plurality of chunks is stored, wherein, subsequent to the            set of data transfers, the first memory portion comprises at            least the particular chunk;        -   implementing the second chunk-level filtering operation on            an in-memory result set of the first chunk-level filtering            operation; and        -   extracting a plurality of observation records from an output            of the sequence of chunk-level filtering operations.    -   7. The method as recited in clause 6, wherein the one or more        data sources comprise one or more storage objects including a        particular storage object, wherein said mapping the particular        data set into the plurality of chunks comprises determining,        based at least in part on a chunk size parameter, a candidate        offset within the particular storage object as a candidate        ending boundary of the particular chunk, further comprising        performing, by the one or more computing devices:    -   selecting, as an ending boundary of the particular chunk, a        particular delimiter representing an ending boundary of a        particular observation record within the particular storage        object, wherein the particular delimiter is located at a        different offset than the candidate offset.    -   8. The method as recited in clause 7, wherein said selecting, as        the ending boundary, the particular delimiter comprises:    -   identifying, in a sequential read of the particular storage        object in order of increasing offsets, the first delimiter with        an offset higher than the candidate offset as the ending        boundary of the particular chunk.    -   9. The method as recited in any of clauses 6-7, wherein the one        or more data sources comprise one or more of: (a) a single-host        file system, (b) a distributed file system, (c) a storage object        accessible via a web service interface from a network-accessible        storage service, (d) a storage volume presenting a block-level        device interface, or (e) a database.    -   10. The method as recited in any of clauses 6-7 or 9, wherein        the request is formatted in accordance with an application        programming interface of the machine learning service.    -   11. The method as recited in any of clauses 6-7 or 9-10, further        comprising performing, by the one or more computing devices:    -   de-compressing contents of the particular chunk in accordance        with one or more de-compression parameters indicated in the        request.    -   12. The method as recited in any of clauses 6-7 or 9-11, further        comprising performing, by the one or more computing devices:    -   decrypting contents of the particular chunk in accordance with        one or more decryption parameters indicated in the request.    -   13. The method as recited in any of clauses 6-7 or 9-12, wherein        the plurality of observation records comprises a first        observation record of a first record length, and a second        observation record of a different record length.    -   14. The method as recited in any of clauses 6-7 or 9-13, further        comprising performing, by the one or more computing devices:    -   implementing an intra-chunk filtering operation on a set of        observation records identified within the particular chunk.    -   15. The method as recited in any of clauses 6-7 or 9-14, further        comprising performing, by the one or more computing devices:    -   inserting a first job object representing the first chunk-level        filtering operation in a collection of jobs to be scheduled at        the machine learning service; and    -   inserting a second job object representing the second        chunk-level filtering operation in the collection, prior to a        completion of the first chunk-level filtering operation.    -   16. The method as recited in any of clauses 6-7 or 9-15, further        comprising performing, by the one or more computing devices:    -   providing the plurality of observation records extracted from        the output of the sequence as input for an execution of one or        more of: (a) a feature processing recipe or (b) a machine        learning model.    -   17. A non-transitory computer-accessible storage medium storing        program instructions that when executed on one or more        processors:    -   generate in response to receiving a request to extract        observation records of a particular data set from one or more        data sources at a machine learning service, a plan to perform        one or more chunk-level operations including a first chunk-level        operation on a plurality of chunks of the particular data set,        wherein an operation type of the first chunk-level operation        comprises one or more of: (a) sampling, (b) shuffling, (c)        splitting, or (d) partitioning for parallel computation;    -   initiate, to implement the first chunk-level operation, a set of        data transfers directed to one or more persistent storage        devices at which at least a subset of the plurality of chunks is        stored, wherein, subsequent to the set of data transfers, a        first memory portion of a particular server of the machine        learning service comprises at least a particular chunk of the        plurality of chunks; and    -   implement a second operation on a result set of the first        chunk-level operation, wherein the second operation comprises        one or more of: (a) another filtering operation, (b) a feature        processing operation or (c) an aggregation operation.    -   18. The non-transitory computer-accessible storage medium as        recited in clause 17, wherein the particular data set comprises        contents of one or more of: (a) a single-host file system, (b) a        distributed file system, (c) a storage object accessible via a        web service interface from a network-accessible storage        service, (d) a storage volume presenting a block-level device        interface, or (e) a database.    -   19. The non-transitory computer-accessible storage medium as        recited in any of clauses 17-18, wherein the second operation        comprises an intra-chunk filtering operation.    -   20. The non-transitory computer-accessible storage medium as        recited in any of clauses 17-19, wherein the second operation        comprises a cross-chunk filtering operation performed on a        plurality of observation records including a first observation        record identified within the particular chunk and a second        observation record identified within a different chunk of the        plurality of chunks.    -   21. The non-transitory computer-accessible storage medium as        recited in any of clauses 17-20, wherein the second operation is        an in-memory operation performed without copying the particular        chunk to a different persistent storage device and without        re-reading contents of the particular chunk from the one or more        persistent storage devices.    -   22. The non-transitory computer-accessible storage medium as        recited in any of clauses 17-21, wherein the operation type of        the first chunk-level operation is partitioning for a parallel        computation, wherein the first chunk-level operation includes a    -   plurality of model training operations including a first        training operation and a second training operation, wherein an        execution duration of the first training operation overlaps at        least in part with an execution duration of the second training        operation.

Embodiments of the disclosure can also be described in view of thefollowing clauses:

-   -   1. A system, comprising:    -   one or more computing devices configured to:        -   generate consistency metadata to be used for one or more            training-and-evaluation iterations of a machine learning            model, wherein the consistency metadata comprises at least a            particular initialization parameter value for a            pseudo-random number source;        -   sub-divide an address space of a particular data set of the            machine learning model into a plurality of chunks, including            a first chunk comprising a first plurality of observation            records, and a second chunk comprising a second plurality of            observation records;        -   retrieve, from one or more persistent storage devices,            observation records of the first chunk into a memory of a            first server, and observation records of the second chunk            into a memory of a second server,        -   select, using a first set of pseudo-random numbers, a first            training set from the plurality of chunks, wherein the first            training set includes at least a portion of the first chunk,            wherein observation records of the first training set are            used to train the machine learning model during a first            training-and-evaluation iteration of the one or more            training-and-evaluation iterations, and wherein the first            set of pseudo-random numbers is obtained using the            consistency metadata; and        -   select, using a second set of pseudo-random numbers, a first            test set from the plurality of chunks, wherein the first            test set includes at least a portion of the second chunk,            wherein observation records of the first test set are used            to evaluate the machine learning model during the first            training-and-evaluation iteration, and wherein the second            set of pseudo-random numbers is obtained using the            consistency metadata.    -   2. The system as recited in clause 1, wherein the one or more        computing devices are further configured to:    -   insert a first job corresponding to the selection of the first        training set in a collection of jobs to be scheduled at of a        machine learning service, and a second job corresponding to the        selection of the first test set in the collection; and    -   schedule the second job for execution asynchronously with        respect to the first job.    -   3. The system as recited in any of clauses 1-2, wherein the one        or more computing devices are configured to:    -   receive, from a client of a machine learning service, a request        for the one or more training-and-evaluation iterations, wherein        the request indicates at least a portion of the consistency        metadata.    -   4. The system as recited in any of clauses 1-3, wherein the        consistency metadata is based at least in part on an identifier        of a data object in which one or more observation records of the        particular data set are stored.    -   5. The system as recited in any of clauses 1-4, wherein the one        or more computing devices are further configured to:    -   reorder observation records of the first chunk prior to        presenting the observation records of the first training set as        input to the machine learning model.    -   6. A method, comprising:    -   one or more computing devices configured to:        -   determining consistency metadata to be used for one or more            training-and-evaluation iterations of a machine learning            model, wherein the consistency metadata comprises at least a            particular parameter value for a pseudo-random number            source;        -   sub-dividing an address space of a particular data set of            the machine learning model into a plurality of chunks,            including a first chunk comprising a first plurality of            observation records, and a second chunk comprising a second            plurality of observation records;        -   selecting, using the consistency metadata, a first training            set from the plurality of chunks, wherein the first training            set includes at least a portion of the first chunk, and            wherein observation records of the first training set are            used to train the machine learning model during a first            training-and-evaluation iteration of the one or more            training-and-evaluation iterations; and        -   selecting, using the consistency metadata, a first test set            from the plurality of chunks, wherein the first test set            includes at least a portion of the second chunk, and wherein            observation records of the first test set are used to            evaluate the machine learning model during the first            training-and-evaluation iteration.    -   7. The method as recited in clause 6, further comprising        performing, by the one or more computing devices:    -   retrieving, from a persistent storage device into a memory of a        first server, at least the first chunk prior to training the        machine learning model during the first training-and-evaluation        iteration; and    -   selecting, for a different training-and-evaluation iteration of        the one or more training-and-evaluation iterations, (a) a        different training set and (b) a different test set, without        copying the first chunk from the memory of the first server to a        different location.    -   8. The method as recited in any of clauses 6-7, further        comprising performing, by the one or more computing devices:    -   receiving, from a client of a machine learning service, a        request for the one or more training-and-evaluation iterations,        wherein the request indicates at least a portion of the        consistency metadata.    -   9. The method as recited in clause 8, wherein the request is        formatted in accordance with a particular programmatic interface        implemented by a machine learning service of a provider network.    -   10. The method as recited in any of clauses 6-8, wherein the        consistency metadata is based at least in part on an identifier        of a data object in which one or more observation records of the        particular data set are stored.    -   11. The method as recited in any of clauses 6-8 or 10, wherein        the first training set comprises at least one observation record        of a third chunk of the plurality of chunks, and wherein the        first test set comprises at least one observation record of the        third chunk.    -   12. The method as recited in any of clauses 6-8 or 10-11,        further comprising performing, by the one or more computing        devices:    -   shuffling observation records of the first chunk prior to        presenting the observation records of the first training set as        input to the machine learning model.    -   13. The method as recited in any of clauses 6-8 or 10-12,        further comprising performing, by the one or more computing        devices:    -   determining a number of chunks into which the address space is        to be sub-divided based at least in part on one or more of: (a)        a size of available memory at a particular server or (b) a        client request.    -   14. The method as recited in any of clauses 6-8 or 10-13,        wherein the particular data set is stored in a plurality of data        objects, further comprising:    -   determining an order in which the plurality of data objects are        to be combined prior to sub-dividing the address space.    -   15. The method as recited in any of clauses 6-8 or 10-14,        wherein the one or more training-and-evaluation iterations are        cross-validation iterations of the machine learning model.    -   16. A non-transitory computer-accessible storage medium storing        program instructions that when executed on one or more        processors:    -   determine consistency metadata to be used for one or more        training-and-evaluation iterations of a machine learning model,        wherein the consistency metadata comprises at least a particular        parameter value for a pseudo-random number source;    -   select, using the consistency metadata, a first training set        from a plurality of chunks of a particular data set, wherein        individual ones of the plurality of chunks comprise one or more        observation records, wherein the first training set includes at        least a portion of a first chunk of the plurality of chunks, and        wherein observation records of the first training set are used        to train the machine learning model during a first        training-and-evaluation iteration of the one or more        training-and-evaluation iterations; and    -   select, using the consistency metadata, a first test set from        the plurality of chunks, wherein the first test set includes at        least a portion of a second chunk of the plurality of chunks,        and wherein observation records of the first test set are used        to evaluate the machine learning model during the first        training-and-evaluation iteration.    -   17. The non-transitory computer-accessible storage medium as        recited in clause 16, wherein the instructions when executed on        the one or more processors:    -   initiate a retrieval, from a persistent storage device into a        memory of a first server, of at least the first chunk prior to        training the machine learning model during the first        training-and-evaluation iteration; and    -   select, for a different training-and-evaluation iteration of the        one or more training-and-evaluation iterations, (a) a different        training set and (b) a different test set, without copying the        first chunk from the memory of the first server to a different        location.    -   18. The non-transitory computer-accessible storage medium as        recited in any of clauses 16-17, wherein the instructions when        executed on the one or more processors:    -   receive, from a client of a machine learning service, a request        for the one or more training-and-evaluation iterations, wherein        the request indicates at least a portion of the consistency        metadata.    -   19. The non-transitory computer-accessible storage medium as        recited in any of clauses 16-18, wherein the consistency        metadata is based at least in part on an identifier of a data        object in which one or more observation records of the        particular data set are stored.    -   20. The non-transitory computer-accessible storage medium as        recited in in any of clauses 16-19, wherein the instructions        when executed on the one or more processors:    -   shuffle observation records of the first chunk prior to        presenting the observation records of the first training set as        input to the machine learning model.

Embodiments of the disclosure can also be described in view of thefollowing clauses:

-   -   1. A system, comprising:    -   one or more computing devices configured to:        -   receive, at a machine learning service implemented at a            provider network, an indication of a data set to be used to            train one or more machine learning models on behalf of a            client of the service, wherein the data set comprises a            plurality of observation records;        -   identify, based at least in part on respective values of one            or more dependent variables indicated in at least a subset            of the plurality of observation records, a plurality of            categories of observation records including a majority            category and one or more minority categories;        -   determine, based at least in part on one or more of: (a) an            imbalance criterion or (b) a performance goal, that a            respective sample of a plurality of categories of the            observation records of the data set, including a first            category and a second category, is to be generated to train            a particular machine learning model;        -   select a first sampling ratio to be used for sampling the            first category, and a second sampling ratio to be used for            sampling the second category, wherein at least one sampling            ratio of the first and second sampling ratios is not            indicated by a client on whose behalf the particular machine            learning model is to be trained;        -   identify respective sampling methodologies to be applied to            the first category and the second category to obtain            respective samples in accordance with the first and second            sampling ratios;        -   train the particular machine learning model using at least a            result of applying the respective sampling methodologies to            the first category and the second category; and        -   provide an indication of the particular machine learning            model to the client.    -   2. The system as recited in clause 1, wherein at least one        sampling ratio of the first and second sampling ratios is        selected based at least in part on a problem domain for which        the particular machine learning model is to be trained.    -   3. The system as recited in any of clauses 1-2, wherein at least        one sampling ratio of the first and second sampling ratios is        selected based at least in part on a memory size constraint of a        particular server of the machine learning service.    -   4. The system as recited in any of clauses 1-3, wherein the        performance goal comprises one or more of: (a) a model training        time goal (b) a model execution time goal (c) a model storage        requirement goal (d) a resource utilization goal pertaining to        one or more resources of the machine learning service (e) a        budget goal indicated by the client, or (f) a prediction        accuracy goal.    -   5. The system as recited in any of clauses 1-4, wherein the        particular machine learning model is one of: a regression model,        a decision tree based model, a linear model, or a non-linear        model.    -   6. A method, comprising:    -   performing, by one or more computing devices:        -   identifying, at a machine learning service, a majority            category of observation records of a training data set to be            used for a particular machine learning model, and one or            more minority categories of observation records of the            training data set;        -   determining that the training data set meets one or more            criteria for automated sampling;        -   identifying a sampling ratio to be used for a particular            category of the majority category and the one or more            minority categories, wherein the sampling ratio is not            indicated by a client on whose behalf the particular machine            learning model is to be trained;        -   applying a selected sampling methodology to the particular            category to obtain a sample in accordance with the sampling            ratio; and        -   training the particular machine learning model using a            result of applying at least the selected sampling            methodology on the particular category.    -   7. The method as recited in clause 6, wherein the particular        category is a minority category.    -   8. The method as recited in clause 6, wherein the particular        category is the majority category.    -   9. The method as recited in any of clauses 6-8, further        comprising performing, by the one or more computing devices:    -   receiving a model training request formatted in accordance with        a programmatic interface implemented by the machine learning        service, wherein the training data set is indicated in the model        training request.    -   10. The method as recited in any of clauses 6-9, wherein the        sampling ratio is based at least in part on a problem domain        indicated by the client.    -   11. The method as recited in any of clauses 6-10, wherein the        sampling ratio is based at least in part on a memory size        constraint of a particular server of the machine learning        service.    -   12. The method as recited in any of clauses 6-11, wherein the        sampling ratio is based at least in part on one or more of: (a)        a model training time goal, (b) a model execution time goal, (c)        a model storage requirement goal, (d) a resource utilization        goal pertaining to one or more resources of the machine learning        service, (e) a budget goal indicated by the client, or (f) a        prediction accuracy goal.    -   13. The method as recited in any of clauses 6-12, further        comprising performing, by the one or more computing devices:    -   selecting the sampling methodology from among a plurality of        methodologies including at least one of: (a) a random sampling        methodology with no replacement, or (b) a random sampling        methodology with replacement.    -   14. The method as recited in any of clauses 6-13, wherein the        particular machine learning model is one of: a regression model,        a decision tree based model, a linear model, or a non-linear        model.    -   15. The method as recited in any of clauses 6-14, wherein the        training data set includes class labels generated by an        unsupervised learning model.    -   16. A non-transitory computer-accessible storage medium storing        program instructions that when executed on one or more        processors:    -   determine, at a machine learning service, that a training data        set comprising a majority category of observation records and        one or more minority categories of observation records meets one        or more criteria for automated sampling;    -   identify a sampling ratio to be used for a particular category        of the majority category and the one or more minority        categories;    -   apply a selected sampling methodology to the particular category        to obtain a sample in accordance with the sampling ratio; and    -   train a machine learning model using a result of applying the        selected sampling methodology on the particular category.    -   17. The non-transitory computer-accessible storage medium as        recited in clause 16, wherein the particular category is a        minority category.    -   18. The non-transitory computer-accessible storage medium as        recited in an of clauses 16-17, wherein a particular criterion        of the one or more criteria is based at least in part on a        problem domain for which the particular machine learning model        is to be used.    -   19. The non-transitory computer-accessible storage medium as        recited in any of clauses 16-18, wherein the sampling ratio is        based on one or more of: (a) a model training time goal, (b) a        model execution time goal, (c) a model storage requirement        goal, (d) a resource utilization goal pertaining to one or more        resources of the machine learning service, (e) a budget goal        indicated by the client, or (f) a prediction accuracy goal.    -   20. The non-transitory computer-accessible storage medium as        recited in any of clauses 16-19, wherein the machine learning        model is one of: a regression model, a decision tree based        model, a linear model, or a non-linear model.

CONCLUSION

Various embodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Generally speaking, acomputer-accessible medium may include storage media or memory mediasuch as magnetic or optical media, e.g., disk or DVD/CD-ROM, volatile ornon-volatile media such as RAM (e.g. SDRAM, DDR, RDRAM, SRAM, etc.),ROM, etc., as well as transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as network and/or a wireless link.

The various methods as illustrated in the Figures and described hereinrepresent exemplary embodiments of methods. The methods may beimplemented in software, hardware, or a combination thereof. The orderof method may be changed, and various elements may be added, reordered,combined, omitted, modified, etc.

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended to embrace all such modifications and changes and, accordingly,the above description to be regarded in an illustrative rather than arestrictive sense.

What is claimed is:
 1. A system, comprising: one or more computing devices that include one or more processors, configured to: receive, at a machine learning service implemented at a provider network, an indication of a data set to be used to train one or more machine learning models on behalf of a client of the service, wherein the data set comprises a plurality of observation records; analyze at least a subset of the observation records in the data set to: identify, based at least in part on respective values of one or more dependent variables indicated in the analyzed observation records, a plurality of categories of observation records including a majority category and one or more minority categories; and determine a population ratio between a number of observation records in the majority category and a number of observation records in one of the one or more minority categories, and that the population ratio is greater than a threshold ratio; determine, based at least in part on the determination that the population ratio is greater than the threshold ratio, to use a category-dependent sampling of the data set that selects from the data set respective samples of individual categories of the plurality of categories, including a first category and a second category, to train a particular machine learning model; select a first sampling ratio to be used for sampling the first category of the observation records in the data set, and a second sampling ratio to be used for sampling the second category of the observation records in the data set, wherein the first and second sampling ratios indicate respective fractions of observations records of the first and second categories, and at least one sampling ratio of the first and second sampling ratios is not indicated by a client on whose behalf the particular machine learning model is to be trained; identify respective sampling methodologies to be applied to the first category and the second category to obtain the respective samples in accordance with the first and second sampling ratios; train the particular machine learning model using at least a result of applying the respective sampling methodologies to the first category and the second category; and provide an indication of the particular machine learning model to the client.
 2. The system as recited in claim 1, wherein at least one sampling ratio of the first and second sampling ratios is selected based at least in part on a problem domain for which the particular machine learning model is to be trained.
 3. The system as recited in claim 1, wherein at least one sampling ratio of the first and second sampling ratios is selected based at least in part on a memory size constraint of a particular server of the machine learning service.
 4. The system as recited in claim 1, wherein the system is configured to determine to generate the respective samples of the categories to train the particular machine learning model based at least in part on a performance goal that comprises one or more of: (a) a model training time goal (b) a model execution time goal (c) a model storage requirement goal (d) a resource utilization goal pertaining to one or more resources of the machine learning service (e) a budget goal indicated by the client, or (f) a prediction accuracy goal.
 5. The system as recited in claim 1, wherein the particular machine learning model is one of: a regression model, a decision tree-based model, a linear model, or a non-linear model.
 6. A method, comprising: performing, by one or more computing devices: identifying, at a machine learning service, a majority category of observation records of a training data set to be used for a particular machine learning model, and one or more minority categories of observation records of the training data set; determining a population ratio between a number of observation records in the majority category and a number of observation records in one of the one or more minority categories; and determining that the population ratio is greater than a threshold ratio to trigger an automated category-dependent sampling of the training data set for training the machine learning model; identifying a sampling ratio to be used for a particular category of the majority category of observation records in the training data set and the one or more minority categories of observation records in the training data set, wherein the sampling ratio indicates a fraction of observations records in the particular category and is not indicated by a client on whose behalf the particular machine learning model is to be trained; applying a selected sampling methodology to the particular category to obtain a sample in accordance with the sampling ratio; and training the particular machine learning model using a result of applying at least the selected sampling methodology on the particular category.
 7. The method as recited in claim 6, wherein the particular category is a minority category.
 8. The method as recited in claim 6, wherein the particular category is the majority category.
 9. The method as recited in claim 6, further comprising performing, by the one or more computing devices: receiving a model training request formatted in accordance with a programmatic interface implemented by the machine learning service, wherein the training data set is indicated in the model training request.
 10. The method as recited in claim 6, wherein the sampling ratio is based at least in part on a problem domain indicated by the client.
 11. The method as recited in claim 6, wherein the sampling ratio is based at least in part on a memory size constraint of a particular server of the machine learning service.
 12. The method as recited in claim 6, wherein the sampling ratio is based at least in part on one or more of: (a) a model training time goal, (b) a model execution time goal, (c) a model storage requirement goal, (d) a resource utilization goal pertaining to one or more resources of the machine learning service, (e) a budget goal indicated by the client, or (f) a prediction accuracy goal.
 13. The method as recited in claim 6, further comprising performing, by the one or more computing devices: selecting the sampling methodology from among a plurality of methodologies including at least one of: (a) a random sampling methodology with no replacement, or (b) a random sampling methodology with replacement.
 14. The method as recited in claim 6, wherein the particular machine learning model is one of: a regression model, a decision tree based model, a linear model, or a non-linear model.
 15. The method as recited in claim 6, wherein the training data set includes class labels generated by an unsupervised learning model.
 16. A non-transitory computer-accessible storage medium storing program instructions that when executed on one or more processors: determine, at a machine learning service and in a training data set for training a machine learning model, a majority category of observation records and one or more minority categories of observation records; determine a population ratio between a number of observation records in the majority category and a number of observation records in one of the one or more minority categories, and that the population ratio is greater than a threshold ratio for triggering an automated category-dependent sampling of the training data set for training the machine learning model; identify a sampling ratio to be used for a particular category of the majority category of observation records in the training data set and the one or more minority categories of observation records in the training data set, wherein the sampling ratio indicates a fraction of observations records in the particular category and is not indicated by a client on whose behalf the machine learning model is to be trained; apply a selected sampling methodology to the particular category to obtain a sample in accordance with the sampling ratio; and train the machine learning model using a result of applying the selected sampling methodology on the particular category.
 17. The non-transitory computer-accessible storage medium as recited in claim 16, wherein the particular category is a minority category.
 18. The non-transitory computer-accessible storage medium as recited in claim 16, wherein the threshold ratio is part of an imbalance criterion that triggers the automated category-dependent sampling of the training data set for training the machine learning model and determined based at least in part on a problem domain of machine learning model.
 19. The non-transitory computer-accessible storage medium as recited in claim 16, wherein the sampling ratio is based on one or more of: (a) a model training time goal, (b) a model execution time goal, (c) a model storage requirement goal, (d) a resource utilization goal pertaining to one or more resources of the machine learning service, (e) a budget goal indicated by the client, or (f) a prediction accuracy goal.
 20. The non-transitory computer-accessible storage medium as recited in claim 16, wherein the machine learning model is one of: a regression model, a decision tree-based model, a linear model, or a non-linear model. 